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Production of Single Cell Protein by the fermentation biotechnology for Animal Feeding

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Production of Single Cell Protein by the fermentation biotechnology for Animal Feeding

DOI: https://doi.org/10.52091/EVIK-2022/2-3-ENG

Received: October 2021 - Accepted: March 2022

Authors

1 Széchenyi István University, Faculty of Agricultural and Food Sciences, Department of Water and Environmental Sciences
2 SISAF Nanotechnology Drug Delivery, Ulster University
* Corresponding Author: Judit Molnár: Széchenyi István University, Faculty of Agricultural and Food Sciences, Department of Water and Environmental Sciences

Keywords

Kwashiorkor, single cell protein, food by-products, animal feeding, fermentation, biotechnology

1. Summary

Background: Fermentation is a sort of biotechnology that uses microorganisms to produce animal food through chemical process. In ancient times, wastes were treated with chemicals, but now companies convert wastes to valuable food, food ingredients or feed products such as single cell oils or single cell protein. The most used substrate is molasses and corn steep liquor which is a part of the fermentation process.

Aim: The aims of the manuscript is to provide an overview of the yeast strains and food by-products used in production of single cell proteins by fermentation process. Furthermore, the manuscript summarizes the role of single cell protein in animal feed.

Methods: Electronic searches were conducted on Google Scholar database Medline and PubMed. A further search was conducted on the Food and agricultural organisation FAO research article database.

Results: Single cell protein produced by these substrates and different microorganisms (algae, yeast, bacteria) play an important role in animal feeding. Furthermore, SCP is a high-quality protein, unsaturated fatty acids, vitamins and minerals sources for animals.

Conclusion: Production of single cell of protein through the fermentation has several significant benefits including sustainability, health and production efficacy.

2. Introduction

In ancient times, wastes were treated by various chemicals, but this method wasn’t the best. As the worldwide population grows, over recent decades, both animal and dairy production have been increasing steadily. The world now produces more than 350 million tonnes of animal-derived protein, and this value will rise up to around 1250 million tonnes by 2050, to meet global demand for animal-based protein [1]. Now, a lot of company convert various wastes into useful food, food ingredients or feed products for human nutrition and animal feeding. These products are also environment friendly and healthy such as biogas, biofuels, bioenergy. Therefore, different methods and techniques are providing opportunity to develop these products as single cell oils, single cell protein, chemicals, enzymes and many others.

Following the carbohydrate and fat, protein is the major macronutrient, which the body requires in large amount. It is an essential factor for growth, repair of the body and maintenance of health. All of the proteins are made up of the 20 amino acids, and they determine the nutrition values of protein. Some of amino acids cannot be synthesized by humans but are still essential (valine, leucine, isoleucine, phenylalanine, tryptophan, lysine, histidine, methionine and threonine) and must be obtained from our diet. The general structure of amino acids is shown in the Figure 1.

Protein digestion begins in the stomach and continues in the lumen of the intestine and so the proteins are degraded into mono and di amino acids. Those amino acids are absorbed by specific transporters in the intestines, and then released into the blood for use by other tissues, that are considered as the fundamental building blocks of proteins in the body, and they serve as the nitrogenous backbones for compounds like neurotransmitters, enzymes and hormones [2, 3]. Although, both the plant and animal proteins are similar in components, both contain the nearly the same amino acids, but the animal protein contains all the essential amino acids [4].

In general, the human body needs between 1.0 g to 1.5g of protein for each kilogram of weigh in children and adults respectively [5]. If there is insufficient protein in diet chronically that could cause kwashiorkor disease, which is a severe form of malnutrition [6].

Figure 1. General formula for an amino acid: amino group (-NH2), carboxyl group (-COOH) and replaceable group (-R) [7]

Single cell protein (SCP) is one of the high qualities and valuable dietary products from wastes [8, 9, 10, 11, 12]. SCP is a biomass which is produced by different microorganisms and it can also be termed as bio-protein, microbial protein or biomass. These microorganisms can be used as protein-rich ingredients in human and animal diet as well [8]. Furthermore, the SCP can be a good alternative to plant protein sources, and it can be produced throughout the year. In addition, they don’t emit greenhouse gases. The most important thing is the selection of cheap and suitable substrates or agro-industrial by-products and valuable microorganisms to produce protein and reduce the production cost of single cell proteins [8, 13, 14, 15, 16, 17]. In order to achieve this, different substrates were used as apple pomace, yam peels, citrus pulp, potato peels, pineapple waste, papaya waste [8]. However, the most used by-products are molasses and corn steep liquor. It is also important to choose microorganisms for research and industrial purpose as well.

This manuscript focuses on single cell proteins produced by microorganisms (algae, yeast, bacteria) as an alternative protein source. Due to the favorable content values of the single cell protein produced by fermentation (protein, vitamin, mineral), it can be used in digestible form for human nutrition, especially with vitamin supplementation and this contributes to the protection and treatment of malnutrition as a functional food and functional food ingredient [10].

3. Material and method

Electronic searches were conducted on Google Scholar database, Medline and PubMed. A further search was conducted on internet. The search items included, nutrition, dietary, protein, single cell protein, immune system. This review was conducted to analyse the recent literature to show the impact of nutrition, and single cell protein on the dietary system.

4. Result

4.1. Single cell protein produced by fermentation

Single cell protein (SCP) is a protein from cultivated microbial biomass and it can be used for protein supplementation. The SCP fermentation process can be seen in Figure 2. Agricultural and industrial wastes used as substrate to yield SCP. Algae, fungi and bacteria are all the main sources of microbial protein that can be utilized as SCP (Table 1) [18]. In addition, the acceptability of species as food depends on the growth rate, substrate used, contamination, associated toxins. The produced biomass is rich in proteins, amino acids as lysine and methionine, unsaturated fatty acids, vitamins and minerals. Therefore, these are used as food, food supplements [18] and animal feed in the world.

Figure 2. Producing single cell protein by fermentation technology (Modified scheme [8])
Table 1. Single cell protein (biomass) production from microorganisms and different substrates

4.2. Use of food by-products for the production of biomass, in particular molasses and corn steep liquor

Food loss and waste reduction is an important way to reduce costs of production, increase the food system capacity and is also a way to join the environmental sustainability campaign. Food waste also contains several biodegradable components for pathogenic microorganisms that can cause communicable diseases. Thus, food loss and waste reductions also have a positive effect on the well-being and health of the consumers. Therefore, the European Union (EU) is promoting the reduction of food wastes and these food by-products from vegetables, fruits, beverages, sugar, meat, aquaculture and seafood also contain functional or bioactive components. The food by-products can be used in nutraceutical or pharmaceutical industries. These can be transformed by fermentation biotechnology into animal feed products [30]. One of the most used food by-products are molasses and corn steep liquor. Molasses (M) is a by-product of sugar cane and it contains several compounds for fermentation for example vitamins, minerals, sucrose and organic compounds. In addition, corn steep liquor (CSL) is a by-product of the corn wet milling industry and it is rich in several components such as vitamins, minerals, amino acids and proteins. Furthermore, the CSL is also an important source of nitrogen [31]. The used molasses and corn steep liquor as a substrate in the fermentation process can be seen in Table 2

Table 2. Summary of literature references of the beneficial effects of molasses and corn steep liquor

4.3. Role of single cell protein produced by fermentation in animal feeding

The high quality and high protein rich human food and animal feed important to increase with the global population grows. Single cell protein (SCP) products based on microbial biomass, have a potential ingredient to this need [42]. The SCP contains high quality omega-3 fatty acids, vitamins, micronutrients, protein and other useful component for animal body. These valuable components can be seen in Table 3.

Table 3. Valuable components in single cell protein from different microorganisms [42]

Single cell proteins in animal feed supplement protein requirements well in addition to conventional feeds. This can also affect the quality of products of animal origin. The role of single cell proteins in animal feed is confirmed by several manuscripts, which are shown in Table 4.

Table 4. The role of single cell proteins in animal feed

7. References

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[37] Karigidi K. O., Olaiya C. O. (2020): Antidiabetic activity of corn steep liquor extract of Curculigo pilosa and its solvent fractions in streptozotocin-induced diabetic rats. Journal of Traditional and Complementery Medicine. 10. pp. 555-564. DOI

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Effect of a compound bio-preservative on microbiological indicators and shelf life of fresh pork chops

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Effect of a compound bio-preservative on microbiological indicators and shelf life of fresh pork chops

DOI: https://doi.org/10.52091/EVIK-2022/2-5-ENG

Received: December 2021 – Accepted: March 2022

Authors

1 South Ural State University
2 South Ural State Agrarian University
3 LLC "Antey"

Keywords

preservative, antioxidant, fresh meat products, total viable count, yeast, shelf life, storage, microbiology

1. Summary

The article deals with the study of the effect that a compound preservative produces on microbiological indicators and shelf life of fresh pork products. The effect of various preservatives on the total viable count and yeast growth in fresh meat during storage was studied. Experimental studies have shown that the compounds of additive a preservative mixture* actively inhibits microorganism growth during the fresh pork chops storage. In the control sample, the number of microorganisms on the seventh day of storage was 12*104 CFU/g, and, in the sample with the compound additive preservative mixture added, it amounted to 0.1*104 CFU/g. The usage of the ready to use preservative mixture allows actively suppressing the yeast reproduction during long-term storage (seven days) of coarsely chopped fresh pork products (250 CFU/g). The optimal method for applying the preservative to fresh pork chops has been determined. Applying the preservative to coarsely chopped fresh meat by simply mixing and massaging (for example, together with spices or marinades) is the most rational method for this product type. Primary and secondary lipid degradation products are considered, and the peroxide and acid numbers of fresh meat products during 30-day storage are determined. After 30 days of storage, a noticeable increase in oxidative processes in the control sample is observed, whereby the end point of the shelf life of coarsely chopped fresh pork products has been chosen.

*We have to negligate the trade mark name of the preservative mixture by the law of advertisement (The Ed.)

2. Introduction

The problem of efficient preservation of food and raw materials at all stages of their production, storage, transportation and trade, including home food preservation, appears highly relevant today. According to some estimates, up to 25% of the world’s food produced is susceptible to the damaging effects of mold alone [1].

Current methods for preserving food products and preventing their microbiological spoilage are divided into three groups: physical, chemical, and biological ones. Physical methods include temperature (thermal and refrigeration) exposure, drying, vacuuming, etc. Chemical methods comprise salting, smoking, brining, the use of preservatives, etc. Biological ones consist in the treatment with starter and bioprotective cultures, the use of bactericides, enzyme preparations, etc [2, 3]. Each of these methods has certain limitations in the production of a particular product due to their impact on organoleptic properties and nutritional values as well as technical feasibility (for example, need for the required equipment, scarcity of the substances or preparations used). Of all the known methods for preventing microbiological spoilage, chemical preservatives are considered the most easily applicable, quickly feasible, not requiring special equipment and/or changing the manufacturing method [4, 5]. However, the meat industry is rather conservative in terms of the use of food additives, due to the fact, that chemical preservatives are allowed in the production of meat products only in limited quantities, mainly in the manufacture of jellied products and for surface treatment [6]. In addition, consumers overwhelmingly have a negative attitude to meat, labelled as containing preservatives. In this regard, the use of chemical preservatives in the production of meat products is significantly limited and cannot be regarded as universal means to prevent microbiological spoilage.

In Russia, the consumption level and production of pork has been growing rapidly recently. At that, the meat industry is dominated by pork nowadays. Pork production increased by 23% in 2020.

Pork is also a source of complete animal protein and has a high nutritional value. In addition, pork meat contains vitamins, macro- and microelements necessary for a comprehensive development of the human body [7].

For all health benefit properties to be maximally preserved, the rules of processing, transportation and storage of meat have to be observed. According to the Sanitary Rules and Norms SanPiN 2.3.2.1078-01 and Technical Regulations of the Customs Union TRCU 034/2013 “On the safety of meat and meat products”, pork belongs to the category of perishable goods.

If the storage conditions and terms are violated, the growth and reproduction of microorganisms in fresh pork significantly accelerates, which leads to an increase in bacterial contamination. Under favorable conditions, microorganisms accumulate on the surface and gradually penetrate deep into the meat, causing the product spoilage. During storage, meat loses its positive properties, its organoleptic, physical and chemical parameters deteriorate significantly, and the risk of harm to human health increases due to the vital activity of pathogenic microbial flora. There are several types of meat spoilage: putrefaction, slime production, mold formation, acid fermentation (meat souring), etc. The intensity of these processes depends on temperature, relative humidity, microorganism type, and the degree of initial meat contamination [8].

Putrid spoilage is most often found when storage conditions are violated. Putrefactive microflora causes meat spoilage. Putrefactive microorganisms can be both aerobic and anaerobic. They are able to secrete protease enzymes that break down proteins. These microorganisms include aerobic bacilli (B. pyocyaneum, B. mesentericus, B. subtilis, B. megatherium), anaerobic clostridia (Cl. putrificus, Cl. histolyticus, Cl. perfringens, Cl. sporogenes) and facultative anaerobic cocci. The end products of aerobic putrefaction are ammonia, carbon dioxide, hydrogen sulfide, and mercaptans. Each of these compounds can cause harm to a human body, which manifests as a serious intoxication [9].

Anaerobic putrefaction of pork can occur without oxygen. Therefore, even vacuum packing will not protect the meat from spoilage if storage temperature requirements are violated. The end products of anaerobic putrefaction are the products of decarboxylation of amino acids causing the formation of off-odour substances, such as indole, skatol, phenol, cresol, diamines. Their derivatives are cadaveric poisons (cadaverine, putrescine, etc.); they are toxic to humans and can cause death [10].

Slime production is a result of slime-forming microorganisms (lactic acid bacteria, yeast, and micrococci) proliferating and partially dying off on the pork meat surface. The meat storage at a temperature of 18 to 25 oC and high humidity contribute to slime production. However, some microorganisms that cause slime formation can develop even at sub-zero temperatures. During sliming, the meat surface becomes sticky, acquires a gray-green hue and a stale off-odour, the pH of the meat surface layers is 5.2 to 5.3. It is important to distinguish between slime production and the initial stage of putrefaction, as each is caused by a completely different microflora [11].

Another equally dangerous type of meat spoilage is mold formation, which occurs when microscopic fungi develop on the surface during long-term storage of the product. When mold grows, the meat quality decreases because of protein hydrolysis and deamination of amino acids. The fungi most often found on the meat surface are Mucor, Penicillium, Aspergillus and Cladosporium. They are able to grow at low temperatures (in the refrigerators). These fungi produce mycotoxins, cause food spoilage, allergic reactions and various diseases in humans [12, 13].

The goal of this paper is investigating the effect of the compounds of food additives in the preservative mixture (detailed in the section 3.1.) on the resistance of fresh pork to microbiological spoilage during storage.

3. Materials and methods

3.1. Research objects

The research objects in this paper are the follow items:

  • Coarsely chopped fresh pork (with a fat content of not more than 15% by weight)
  • The compounds of food additive preservative mixture. The content of ready to use mixture are potassium sorbate (E202), sodium acetate (E262), sodium benzoate (E211), glycerin (E422), carboxymethylcellulose (E466) and an antioxidant (dihydroquercetin). The additive is manufactured by a research and manufacturing association Russia
  • Lactic acid
  • Acetic acid
  • Sodium acetate (E262)

3.2. Research methodology

The total viable count and the amount of yeast were determined by plating the product onto agar plates with culture media, allowing microorganisms to grow and counting all individual colonies.

The peroxide value and acid value were found using the standard methods [14, 15]. Method for determining the peroxide value is based on the reaction of the oxidation products of animal fats (peroxides and hydroperoxides) with potassium iodide in a solution of acetic acid and isooctane or chloroform, followed by quantitative determination of the released iodine with a solution of sodium thiosulfate using a titrimetric method. The method for determining the acid value is based on the dissolution of a sample in a mixed solvent, and titration of free fatty acids with a solution of potassium hydroxide.

All analyses were repeated in triplicate unless otherwise stated and the average values were calculated. The results are expressed as the mean value ± standard deviation. Significant differences between the mean values at significance level p < 0.05 were identified using the one-way analysis of variance and Student’s test. Microsoft Excel version 2010 was used as the statistical analysis software.

4. Results and discussion

To identify the functional properties of preservative mixture, tests were carried out on chilled pork in comparison with control samples and the most common substances having a preservative effect (lactic acid, acetic acid and sodium acetate). A comparative assessment of microbiological indicators in meat products was carried out, wherefore the quantities of mesophilic aerobic and facultative anaerobic microorganisms (MAFAM) were monitored for 7 days at a temperature of 8 to 10 oC. The experimental results are shown in Figure 1.

Figure 1. Effect of various preservatives on the total viable count of fresh pork chops during storage

The most common indicator of meat chops spoilage is natural acid fermentation. As a rule, acid fermentation develops in muscle tissue rich in glycogen. The main signs of the process are a sour off odour, gray or greenish hue, a decrease in the tissue elasticity and, as a result, a loose consistency. The causative agents of the defect are psychrotrophic lactic acid bacteria and yeast fungi, which ferment carbohydrates to form organic acids, as well as gases (carbon dioxide and, in some cases, hydrogen). In addition to meat carbohydrates, chopped meat products also contain carbohydrates that come from onions, marinades and other ingredients. These carbohydrates located in the brine between pork cuts are a favorable medium for pathogens of acid fermentation to develop.

Our experimental studies have shown that the selected preservatives actively inhibit microbial growth during storage. Thus, on day 7, the microbial content in the control sample was 12·104 CFU/g, in the sample with lactic acid added was 2·104 CFU/g, in the sample with acetic acid added was 1.8·104 CFU/g, in the sample with sodium acetate added was 0.7·104 CFU/g, and in the sample with the compounds of preservative mixture added was 0.1·104 CFU/g.

Preventing the yeast development in meat chops is an important component of raw meat manufacturers’ success, as it directly relates to the shelf life of the product and guarantees its safety for the consumer [16]. The contamination of meat products results from contaminated workers’ hands, storage containers, unsterilized spices and onions. Figure 2 shows the effect that various preservatives produce on the yeast growth in raw meat during storage.

Figure 2. Effect of various preservatives on the yeast growth in fresh pork chops during storage

According to the research results, the classical preservatives (lactic acid, acetic acid and sodium acetate) have a weak effect on the growth and reproduction of yeast during raw pork storage. The fast growth of yeast in fresh pork chops starts on day 2 and reaches its peak value of 1600 CFU/g (control sample) on day 7. However, the use of the compounds of the preservative mixture allows actively suppressing the yeast reproduction during long-term storage of fresh pork products (250 CFU/g).

The data obtained confirm that the preservative mixture not only effectively inhibits the growth of yeast, but also exhibits a clear antimicrobial activity against a wide range of microorganisms.

On studying the microbiological indicators of coarsely chopped fresh pork, it was found that the yeast content on the brined meat cuts surface is hundreds of times higher than in the internal tissue. Given this fact, it is obviously the meat surface as well as the ingredients in the brine that should mostly be exposed to preservatives. In order to verify this statement, the microbiological indicators of raw pork prepared according to the same recipe, but using different methods for applying the preservative, were compared. In one sample, the preservative was applied with a syringe solution; in another one, it was added in a liquid form, being mixed with onions and marinade and then massaged. The control sample was prepared without any preservatives. The experimental results are shown in Figure 3.

Figure 3. Effect of the method of applying a preservative on the yeast growth in fresh pork chops

Massaging was carried out in a meat tumbler “Metat master” in accordance with the tumbling program selected. Tumbling parameters are shown in Table 1.

Table 1. Tumbling parameters

Thus, applying a preservative to raw meat chops by simply mixing and massaging (for example, together with spices or marinades) is the most rational method for this product type. In this case, two positive effects are achieved simultaneously: the preservative concentration in the area affected by yeast increases and the total amount of the preservative in the product decreases, which provides advantages from the points of view of both product safety for consumer health and economic benefit.

The study of the increase in the shelf life of the product preserved using the preservative mixture was carried out at various time periods (from 5 to 30 days), every 5 days the amounts of primary and secondary lipid degradation products being measured and peroxide and acid numbers being determined [17].

The food additive was applied with different mass concentrations (0.1%, 0.4%, 0.6% and 1%).

Based on the data obtained, the dependence of the peroxide number (PN) values of the samples on the product storage duration was determined (Figure 4).

Depending on the dosage of the preservative mixture, the PN values vary, but in all options the increasing dynamics is observed over time. The highest rate is detected in the control sample. The lowest oxidative processes are observed in the samples containing 0.6% and 1% of the preservative and having significant differences with the control sample.

Figure 4. Accumulation of primary oxidation products in fresh pork chops during storage

On day 10, all samples containing preservative mixture exhibit a sharp increase in the PN values (1.6 times on average), which causes the interaction of acetic acid contained in the marinade with the antioxidant (dihydroquercetin), accompanied by a shift in acidity towards the alkaline side. On day 15 of storage, the PN value continues to gradually increase, and the active substance of the food additive begins inhibiting lipid peroxidation throughout the storage period, demonstrating significant differences with the control sample. After 30 days of storage, a noticeable increase in oxidative processes in the control sample is observed, whereby the end point of the shelf life of fresh meat has been chosen. However, the antioxidant activity allows increasing the product storage stability.

Similar dynamics are observed in the acid number variation (Figure 5).

Figure 5. Acid number variation over the entire product shelf life

Oxygenated products with excessive acidity reduce meat quality due to moisture loss. In turn, moderate acidity of meat products reduces the product quality to a lesser degree, so it remains juicy. In addition, the antioxidant contained in preservative mixture allows increasing the shelf life.

5. Conclusions

The novelty of this research is theoretically justified, and a high performance of the compounds of food additive preservative mixture in manufacturing raw pork is experimentally confirmed. Applying the additive allows reducing losses during heat treatment and storage of the meat products, increasing yield and improving consistency, as well as reducing the cost of production and increasing the shelf life of up to 30 days. This result is ensured by the use of the latest broad-spectrum antimicrobial preparation of preservative mixture. The preparation has a significant bactericidal effect and inhibits the growth and development of yeast. The most rational way to apply the preparation into a meat system is to add it to the product by simply mixing and then massaging (for example, with spices or marinades). As the preservative contains the natural antioxidant dihydroquercetin, the preparation can be used to good advantage for products with a high fat content to prevent the lipid fraction oxidation during storage. The optimal concentrations of the preparation in the meat system is from 0.6% to 1% by weight. A higher concentration will lead to a higher price of the end product. Meanwhile, a concentration of the bio-preservative less than 0.6% by weight will reduce the product storage stability and its resistance to microbial spoilage.

6. Conflicts of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the content of this paper.

7. Acknowledgement

The work was supported by Act 211 of the Government of the Russian Federation, contract № 02.A03.21.0011.

8. References

[1] Saucier, L. (2016): Microbial spoilage, quality and safety within the context of meat sustainability. Meat Science, 120, pp. 78–84. DOI

[2] Huffman, R. D. (2002): Current and future technologies for the decontamination of carcasses and fresh meat. Meat Science, 62, pp. 285–294. DOI

[3] Zhang, H. Z., Wu, J., Guo, X. (2015): Effects of antimicrobial and antioxidant activities of spice extracts on raw chicken meat quality. Food Science and Human Wellness, 5, pp. 39-48. DOI

[4] Chen, J. H., Ren, Y., Seow, J., Liu, T., Bang, W. S., Yuk, H. G. (2012): Intervention Technologies for Ensuring Microbiological Safety of Meat: Current and Future Trends. Comprehensive Reviews in Food Science and Food Safety, 11, pp. 119–132.

[5] Naveena, B. M., Sen, A. R., Vaithiyanathan, S., Babji, Y., Kondaiah, N. (2008): Comparative efficacy of pomegranate juice, pomegranate rind powder extract and BHT as antioxidants in cooked chicken patties. Meat Science, 80, pp. 1304–1308, DOI

[6] Aymerich, T., Picouet, P. A., Monfort, J. M. (2008): Meat decontamination technologies for meat products. Meat Science, 78, pp. 114–129. DOI

[7] Lucera, A., Costa, C., Conte, A., Del Nobile, M. A. (2012): Food applications of natural antimicrobial compounds. Frontiers in Microbiology, 3, pp. 1–13. DOI

[8] Russell, S. M. (2009): Understanding poultry spoilage. Last accessed 14th April 2017.

[9] Doulgeraki, A. I., Ercolini, D., Villani, F., Nychas, G. E. (2012): Spoilage microbiota associated to the storage of raw meat in different conditions. The International Journal of Food Microbiology, 157, pp. 130–141. DOI

[10] Soladoye, O. P., Juárez, M. L., Aalhus, J. L., Shand, P., Estévez, M. (2015): Protein oxidation in processed meat: mechanisms and potential implications on human health. Comprehensive Reviews in Food Science and Food Safety, 14, pp. 106–122. DOI

[11] Thomas, C. J., O’Rourke, R. D., McMeekin, T. A. (1987): Bacterial penetration of chicken breast muscle. Food Microbiology, 4(1), pp. 87–95. DOI

[12] Scallan, E., Hoekstra, R. M., Angulo, F. J., Tauxe, R. V., Widdowson, M. A., Roy, S. L., Jones, J. L., Griffin, P. M. (2011): Foodborne illness acquired in the United States-major pathogens. Emerging Infectious Diseases, 17, pp. 7–15. DOI

[13] Waites, W. M. (1998): The microbiology of meat and poultry. Meat Science, 50(1), p. 137. DOI

[14] Antipova, L. V., Glotova, I. A., Rogov, I. A. (2001): Meat and meat products research methods, Moscow, Kolos, pp. 376.

[15] Skurikhin, I. M., Tutelyan, V. A. (1998): Guide to methods for analysis of food quality and safety. Moscow, Brandes, Medicine, pp. 342.

[16] Viljoen, B. C., Geornaras, A., Lamprecht, A., Holy, A. (1998): Yeast populations associated with processed poultry. Food Microbiology, 15, pp. 113-117. DOI

[17] Aminzare, M., Hashemi, M., Ansarian, E., Bimkar, M., Azar, H. H., Mehrasbi, M. R., Daneshamooz, S., Raeisi, M., Jannat, B., Afshari, A. (2019): Using Natural Antioxidants in Meat and Meat Products as Preservatives: A Review. Journal of Animal and Veterinary Advances, 7, pp. 417–426.

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On adulteration of fruit and berry raw materials

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On adulteration of fruit and berry raw materials

DOI: https://doi.org/10.52091/EVIK-2022/1-2-ENG

Received: November 2021 – Accepted: February 2022

Authors

1 South Ural State University (national research university), Chelyabinsk, Russian Federation
2 South Ural State Agrarian University, Troitsk, Russian Federation
3 LLC „Antey”

Keywords

adulteration, fruit and berry raw materials, chemical composition of fruits, organic acids profile, mineral elements.

1. Summary

We studied organoleptic, physical, chemical parameters, and nutrient composition of strawberry, raspberry, and melon powders and identified their profile of organic acids and mineral composition produced by a Russian company. It was found that the color and flavor ranges of the studied materials were uncharacteristic of the initial raw materials. The actual protein and lipids levels did not correspond to the ones declared by the manufacturer in the labeling, and were uncharacteristic of the processed raw materials. In all powder samples the sugars were represented by sucrose in 80-97%. This high level of sucrose content indicated the addition of 40.4-52.3% white sugar. The amount and ratio of organic acids did not correspond to the profile of natural raw materials. Thus, the strawberry powder lacked oxalic and tartaric acids, the raspberry raw material lacked malic acid, and the melon material – citric acid. The strawberry powder above the detection limit did not contain such essential macro- and microelements as Ca, Mg, B, Co, the amount of Si, Fe, K was at trace level. The raspberry powder was devoid of detectable amount of Co and K, and B, Ca, Cu, Mg, Mn, Si important for plant life were present in residual amounts. The “obligatory” amount of K, Fe, Ca, Co, Cu, Mg, Mn were absent in the melon powder, which did not correspond to the fundamental laws of the plant physiology. The results obtained allowed to conclude about misinformation and qualitative adulteration of the materials. Currently, there are practically no studies aimed at determining quality and chemical composition of fruit and berry powders in order to identify adulteration, though this type of survey would be great practical interest both for producers and consumers.

2. Introduction

The modern consumer market of edible raw materials and foods is extremely important strategic part of the modern economy of the Russian Federation. In recent years, the spread of adulterated goods there has reached such a level that it threatens Russia’s national security. Adulteration of agricultural raw materials should be regarded as one of the most dangerous types of fraudulent practices, because it creates favorable conditions for unfair competition, leading to stagnation, loss of export potential of domestic food producers and, consequently, to the decrease in the investment appeal of the industry.

Fresh juicy berries and fruits are natural sources of biologically active substances. However, these are seasonal, perishable products. So, to level the seasonal nature of consumption, increase the shelf life of the finished product and reduce the transportation and storage costs, they are often processed and dried [1, 2].

Strawberry (Fragaria x ananassa, D.) is known as a berry with high content of organic acids (citric, malic, quinic, salicylic, as well as succinic and traces of shikimic and glycolic upon ripening), vitamins C, PP, E, B1, B2, B6, B9, K, carotene, pectin and other substances. Strawberry is rich in phenolic compounds which have antioxidant, anti-inflammatory, and anticancer action [3, 4]. Ripe raspberry (Rubus іdaeus L.) contains free organic acids (citric, malic, salicylic), minerals (Co, Cu, K, Na, Fe, Ca, Mg, P) [1, 5], vitamins (B-group, PP, C, provitamin A), tanning substances [6]. Raspberry has diuretic, choleretic, anti-anemic effect, helps strengthen the walls of blood vessels and promotes intestinal health [14]. Melon fruits (Cucumis melo) contain proteins, carbohydrates (sugars, starch, fiber), organic acids, vitamins (B-group, PP, A, C, β-carotene), minerals (K, Na, Fe, Ca, Mn, Mg, Zn). Melon is especially recommended in case of exhaustion, anemia, atherosclerosis, and some other cardiovascular diseases. Melon enhances the effect of antibiotics reducing their toxicity [7].

Rich chemical composition of dried fruit and berry raw materials allows to use them in the production of dairy and baked goods, confectionery, snacks, salads, ketchups, seasonings in order to enrich them with vitamins, minerals, organic acids, fiber, etc. [8]. Knowing the chemical composition of fruit and berry raw materials, identifying components forming the organoleptic characteristics not only constitutes a prerequisite for the production of competitive products, but also makes it possible to identify adulteration. The purpose of the research was to assess the quality and to identify the chemical composition of fruit and berry powders. Research objectives were to study organoleptic properties, physical and chemical parameters, as well as nutrient composition of fruit and berry powders comparing them with commonly known data; to identify the profile of organic acids and mineral composition of the plant material under study.

3. Materials and methods

The investigated products were fruit powders of strawberry, raspberry and melon produced by a Russian company. According to the declaration of the manufacturer, the composition of these powders is 100% corresponding natural raw materials containing no preservatives, dyes, or artificial flavorings.

Organoleptic characteristics of the fruit powders were studied according to GOST 8756.1-2017. Moisture content was determined according to GOST 33977-2016, fat and protein content – according to MU 4237-86 guidelines, non-volatile acids – according to M 04-47-2012, sugars – according to M 04-69-2011, metal and foreign impurities, contamination with grain pests – according to GOST 15113.2-77, food fibers – using the generally accepted method [9], minerals – according to MUK 4.1.1482-03 and MUK 4.1.1483-03 guidelines. All measurements were carried out in three replications.

4. Results and discussion

Sensory evaluation of the quality of the studied materials showed the following: in appearance, the samples of processed strawberries, raspberries, and melons were finely ground homogeneous loose odorless powders, which is uncharacteristic of each type of the original natural raw material. The colour was identified as intense, uniform throughout the mass of the powders, uncharacteristic of dried products, with the following tones: pink with a gray hue for the strawberry powder, light burgundy for the raspberry powder, and light yellow for the melon powder. A sweet taste was noted in the strawberry and melon, and a sour taste in the raspberry material.

According to the results of physical and chemical study of plant materials, no deviations were found from the normal values. Thus, the moisture content of the powders under study was within the range of 4.2-5.1% (in various literature data, the range is 4-12% [1], no infestation with grain pests or presence of metallic and foreign impurities were found.

Fruits and berries have rich chemical composition, which makes them unique elements of a healthy diet [5]. In this regard, we investigated the main nutrients contained in the studied samples of fruit and berry powders.

To begin with, we compared the obtained test results with the information on the product packaging. We found that the actual levels of protein and lipids content did not correspond to the ones stated in the labeling, which indicates misinformation of the consumers. Thus, the amount of proteins and fats in the strawberry powder was 26 and 3.5 times lower, in the raspberry powder – 8 and 60 times higher, respectively, in the melon powder, contrary, it was slightly higher, as for protein in particular – by 55% (Table 1) than the labelling of the products.

Taking into account the fact that drying significantly increases the concentration of dry substances and, consequently, biologically active components [1, 2], it was determined that not all samples of the plant powders contained protein and fat even within the generally known range for fresh raw materials. For example, the amount of protein and lipids in the strawberry powder should be 7.0 g/100 g and 1.0 g/100 g, respectively [1]. The obtained results were far below.

Table 1. Nutrient Composition of Fruit and Berry Powders

Note: *content indicated on the packaging of fruit and berry powders, **in terms of dry matter. a Karkh et al., 2014, / b Akimov et al., 2020, / c Akimov et al., 2021, / d Sannikova, 2009, / e Erenova, 2010, / f Dulov, 2021, / g Pochitskaya et al., 2019, / h Baygarin et al., 2015, / i Medvedkov et al., 2015.

The most important indicator of the quality of fruits and berries is their sugar content, which depends on both the characteristics of a certain variety and weather conditions in the period of crop formation [5, 7]. It is known that for fresh raspberries, the content of sugars is 4-10 %, for dried berries - 34.5-42.2% [5]. Fresh strawberries contain 7.3-11.7% of sugars, which, as in raspberries, are represented mainly by fructose, glucose, and sucrose; their amount varies from 5.9 to 8.9 % [3, 4]. In the fruits of cultivated melon, the level of sugars is 7.0-21.0% [7, 10].

It was found that the ratio of mono- and disaccharides in the studied raw materials did not correspond to the data obtained by a number of scientists in practical studies [5, 6, 10, 11, 12, 13]. As for sugar content in strawberries, fructose should prevail significantly, in melon – sucrose, whereas in raspberries fructose and glucose content should be equivalent. It was revealed that in all samples of plant materials sugars were 80-97% represented by sucrose, and its high level indicated 40.4-52.3% addition of white sugar. In addition, the quantitative levels of monosaccharides in the strawberry powder did not even fall within the lower limits of their content established for fresh berries.

Plant material is distinguished first of all by the presence of dietary fiber, regular consumption of which contributes to the prevention of overweight and obesity, gastrointestinal, cancer, and cardiovascular diseases.

It was determined that by the content of dietary fiber, the studied samples of vegetable material were closer to the levels of characteristic of fresh juicy berries and fruits, since it is known, for example, that the amount of dietary fiber in dried chopped strawberries is not less than 8.0 g/100 g [5]. In our case the dietary fiber content of our samples were only 3.91±0.20 g/100 g.

It is well known that berry and fruit raw materials are characterized by a specific profile of organic acids and macronutrients, and the analysis of their content allows to determine adulteration or to prove its natural character [8]. So, these characteristics were studied in more detail. According to a number of authors, citric acid predominates in raspberry, while the content of malic acid is significantly lower. Salicylic acid in raspberries, which has bactericidal, antipyretic, and analgesic action, is of particular importance [5, 6]. Strawberries contain malic, benzoic, citric, tartaric, oxalic, succinic, and salicylic acids with the predominance of citric and malic ones [11]. Organic acids in cultivated varieties of melon are represented by malic and succinic acids, whereas citric and glucuronic acids appear during storage [10]. According to the test results, the amount and ratio of organic acids in the studied fruit powders did not correspond to the profile of natural raw materials (Table 2). Thus, oxalic and tartaric acids were absent in the strawberry powder, malic acid – in the raspberry raw material, and citric acid – in the melon material (their concentration stayed below the limit of detection).

Table 2. Profile of Organic Acids and Mineral Elements of Fruit and Berry Powders

Notes: *according to TR CU 021/2011, ** in terms of dry matter.

a Stepanov et al., 2013, / b Karkh et al., 2014, / c Akimov et al., 2020, / d Akimov et al., 2021, / e Sannikova, 2009, / f Erenova, 2010, / g Dulov, 2021, / h Pochitskaya et al., 2019 / i Medvedkov et al., 2015

Strawberries and raspberries are known to be rich in macro- and micronutrients. Thus, 100 g of strawberries cover 330% of the daily demand in Si, 264% in B, 40% in Co; 100 g of raspberries – 120% of the daily demand in Si, 250% in B [11]. Si is involved in the metabolism of most mineral elements and vitamins. It’s lack leads to the decrease of digestibility of Ca, Fe, Co, Mn and metabolic disturbance. B plays an important role in the prevention and treatment of bone disease.

Co is a coenzyme of many enzymes, it activates the metabolism of fats and synthesis of folic acid [11]. The berries also contain Fe, Zn, Mn, Cu, Mo etc. It was determined that the strawberry powder under study did not contain in detectable amount of intrinsic essential macro- and microelements, namely Ca, Mg, B, Co, the amount of Si, Fe, K was at the trace level, indicating that the material was not natural. The raspberry powder turned out to be devoid of Co, K, whereas the amount of B, Ca, Cu, Mg, Mn, Si, important for the plant life, was residual. The mineral composition of melon fruit includes K, Ca, Mg, P, Nа, Fe. K is of extreme importance in the mineral nutrition of melon. The higher level of potassium nutrition increases productivity, disease resistance, accumulation of ascorbic acid and sugars [15]. The content of Fe, which plays a leading role in the formation of red blood cells – carriers of oxygen – is 17 times higher in melon than in milk [16]. When testing the mineral profile of the melon powder, it was found that it lacked the plant physiologically “obligatory” amount of K, Fe, Ca, Co, Cu, Mg, Mn, which does not correspond to the fundamental laws of physiology of the plant itself. The results allowed us to conclude about the qualitative adulteration of this plant material.

5. Conclusions

The results of physical and chemical tests of the studied raw materials showed deviations from the norms. Studying the levels of proteins and fats of products of strawberry, raspberry, and melon powders confirmed the fact of adulteration. The data obtained during organoleptic evaluation of quality and identification of profile of sugars, organic acids, and mineral elements allowed us to conclude that the powders under study were not natural fruit and berry raw materials.

6. Conflicts of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the content of this paper.

7. Thanks

The work was supported by Act 211 of the Government of the Russian Federation, contract No. 02.A03.21.0011.

8. References

[1] Ermolaev, V. A. (2019): Low-temperature vacuum drying as the method of draining of plant raw materials. The Bulletin of KrasGAU, 1 (142), pp. 160-166.

[2] Mizberidze, M. Sh., Chakvetadze, Sh. M., Pruidze, M. R. (2017): Intensification of drying processes of berries in the field of infrared rays. Aeconomics: Economics and Agriculture, 8 (20), p. 5.

[3] Stepanov, V. V., Tikhonov, S. L., Mikryukova, N. V. (2013): The analysis of strawberry’s quality during the storage, grown in vivo and micropropagation. Agrarian Bulletin of the Urals, 12 (118), pp. 58-62.

[4] Karkh, D. A., Stepanov, V. V., Tikhonova, N. V., et al. (2014): Expansion of the fortified foodstuffs production as a basis of food security. Journal of Ural State University of Economics, 1 (51), pp. 118-121.

[5] Akimov, M. Yu., Bessonov, V. V., Kodentsova, V. M., et al. (2020): Biological value of fruits and berries of Russian production. Problems of Nutrition, 89 (4), pp. 220-232. DOI

[6] Akimov, M. Yu., Koltsov, V. A., Zhbanova, E. V., et al. (2021): Nutritional value of promising raspberry varieties. IOP Conf. Series: Earth and Environmental Science, 640, 022078. DOI

[7] Sannikova, T. A. (2009): Scientific foundations of resource-saving, waste-free technology of melon cultivation: dissertation for the degree of Doctor of Agricultural Sciences. Astrakhan. 316 p.

[8] Rudenko, O. S., Kondratiev, N. B., Osipov, M. V., et al. (2020): Evaluation of fruit raw materials chemical composition by the content of organic acids and macronutrients. Proceedings of the Voronezh State University of Engineering Technologies, 82 (2), pp. 146-153. DOI

[9] Skurikhin, I. M., Tutelyan, V. A. (1998): Guide to methods for analysis of food quality and safety. Moscow, Brandes, Medicine, 342 p.

[10] Erenova, B. E. (2010): Scientific basis for the production of products on a religious basis: thesis abstract for the degree of Doctor of Technical Sciences. Almaty, 33 p.

[11] Dulov, M. I. (2021): Harvesting, storage and processing of raspberries and strawberries. Petrozavodsk. In the book: innovative technologies in science and education, pp. 4-24.

[12] Pochitskaya, I. M., Roslyakov, Yu. F., Komarova, N. V., et al. (2019): Sensory Components of Fruits and Berries. Food Processing: Techniques and Technology, 49 (1), pp. 50-61.

[13] Baygarin, E. K., Vedischeva, Yu. V., Bessonov, V. V., et al. (2015): The content of dietary fiber in various food products of plant origin. Problems of Nutrition, 84 (5), p. 15.

[14] Ermolina, G. V., Ermolin, D. V., Zavaliy, A. A., et al. (2018): Substantiation of modes of infrared drying of raspberries and blackberries. Transactions of Taurida Agricultural Science, 14 (177), pp. 112-118.

[15] Kosolapova, G. N. (2006): Biochemical composition of raspberry in conditions of the Kirov region. Agricultural Science Euro-North-East, 8, pp. 47-49.

[16] Medvedkov, E. B., Admaeva, A. M., Erenova, B. E., et al. (2015): Chemical composition of melon fruits of mid-season varieties of Kazakhstan. Agricultural sciences and agro-industrial complex at the turn of the century, 12, pp. 36-43.

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Determination of the nutrient content of crops from different countries

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Determination of the nutrient content of crops from different countries

DOI: https://doi.org/10.52091/EVIK-2022/1-3-ENG

Received: October 2021 – Accepted: January 2022

Authors

1 University of Debrecen, Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Nutritional Science
2 University of Debrecen, Doctoral School of Nutritional and Food Sciences

Keywords

lentils, rice, beans, nutrient content, mineral content, sulfur-nitrogen

1. Summary

The crops commercially available in Hungary show great variety in terms of their county of origin. According to out hypothesis, this diversity is also reflected in value of their nutrient content. In our experiments, the nutrient and mineral content of jasmine rice, lentils and beans from different areas of origin was determined, and the results were analyzed using descriptive statistical methods. The aim of our work was to gather basic data from raw materials from different countries of the world, which can be compared with basic data from Hungary. During the evaluation of the results, a trend-like change in macronutrient amount was observed, while the mineral content of the crops was moderately or strongly variable in several cases. Based on our results, it is recommended that experts update basic data more frequently, given the increasingly globalized nature of the world, and take into account the variability of crops by country of origin.

2. Introduction

The lentil, rice and dried bean varieties commercially available in Hungary show great variety in terms of their county of origin. Shoppers can choose from products from five continents on the shelves of a supermarket. This variability is also reflected in the range of raw materials supplied for communal catering. In order to design the right menu, which can even meet special nutritional needs, it is essential to know the nutrient content with sufficient accuracy, which also includes the mineral content. Food labeling only provides information on the main nutrients, but not on the mineral content. In the course of the study, the nutrient and mineral content of crops originating from different countries and appearing in the wholesale and retail trade in Hungary was determined.

3. General characterization of the studied crops

3.1 Jasmine rice

Rice (Orzyza sativa L.) has been a food since the Neolithic. It reached Europe through the ancient Greeks, Romans and then the Mohammedan peoples [1]. It was first categorized by Carl von Linné in the Species Plantarium in 1753 [2]. The geographical boundaries of current rice production are the latitudes of 53o north and 40o south. In 2018, the world’s total rice production was 782 million tonnes. The largest rice-producing countries are China with 214.08 million tonnes/year, India with 172.58 million tonnes/year and Indonesia with 83 million tonnes/year. Rice production in Hungary was 55-68.5 thousand tonnes/year in the 1970s, 30-47 thousand tonnes/year in the 1980s, 10 thousand tonnes/year since the 1990s [3]. The 1000-grain weight of rice grains is between 12 and 54 g. The quality of rice can also be characterized by the profile index. This parameter characterizes the length and width of the grain, based on which it can be slender (3.0<), medium (3.0-2.1), hemispherical (2.1-1.1) and round (1.0>). Rice is a valuable and popular crop which is well reflected in its more than 8,000 varieties.

Outstanding among the varieties is the long-grained jasmine rice, which, when ready for cooking, has a soft texture and a pleasant aroma. Jasmine rice (KDML 105) produced in the northern and northeastern growing areas of Thailand has an outstanding aroma content [4] and has been bred from the Khao Dow Mali 105 and Kor Kho 15 varieties [5]. Its characteristic is that it grows only once a year, in the rainy season. As a result, the crop ripens at the same time, it is harvested at the same time, and the crop is placed on the market at the same time, resulting in a depressed commercial price. The producer can choose to store his crop (which results in storage costs) or sell it immediately at a lower profit. The nutrient content of jasmine rice is different from that of other rice varieties. According to the database of the US Department of Agriculture, Agricultural Research Service, Food Data Central, it has an energy content of 356 kcal, a protein content of 6.67 g/100g, a fat content of practically zero, and a carbohydrate content of 80 g/100 g [6]. According to the measurements of Chee-Hee Se et al., its energy content is 349 kcal, protein content is 6.98±0.16, carbohydrate content is 79.6±0.30, while the fat content is 0.26±0.07 g/100 g [7]. University of Arkansas student Mills and the instructor Wang in 2020 examined samples from nine varieties native to Thailand but grown in the USA [8].

Their nutrient content measurement results were as follows.

  • Protein content (g/100 g): 7.61±0.01; 7.65±0.01; 8.39±0.02; 10.89±0.15; 6.99±0.03; 7.87±01; 9.09±0.02; 6.87±0.00; 8.41±0.13;
  • Fat content (g/100 g): 0.015±0.00; 0.19±0.00; 0.56±0.02; 0.54±0.01; 0.31±0.01; 0.43±0.01, 0.4±0.01; 0.26±0.01; 0.45±0.01.

The mineral content of rice varieties measured by other authors is shown in Table 1.

Table 1. Mineral content of rice from different sources (mg/kg)

3.2. Lentils

The lentil (LensCulinarisMedik. SSP. Culinaris) is one of the oldest cultivated plants of mankind. It was already cultivated in Central Europe during the Stone Age [9]. It is also mentioned in the Bible, in the first book of Moses (Moses 25:27-34), but stable carbon isotope studies have shown that it was also an important part of the diet in ancient Egypt [10]. Its botanical description in 1787 was carried out by Friedrich Kasimir Medikus, a German physicist and botanist [11]. It is currently grown on five continents, in several countries, including Hungary. According to the United Nations Food and Agriculture Organization (UN FAO), it was grown on about 4.3 million hectares between 2012 and 2014, with an annual world lentil production of 5 million tonnes. In 2017, the size of growing area has already reached 6.5 million hectares [12]. The world’s largest lentil producers are Canada, India and the United States, but Australia is also among the emerging countries. In Europe, the largest lentil-producing countries are Russia, Spain and France. Canada accounts for 40% of world production, India is second with 22% and Turkey is third with 8.1%.

Several varieties of lentils are known. They can be distinguished on the basis of the size of the seed: large, medium and small seed, but also on the basis of the color variation of the seed: brown, yellow, red, black or green lentils. Some varieties have outstanding nutrient content. Masooregy is an Indian large seed red lentil variety. Cultivated by Bahauddin Zakariya University in Pakistan, Masoor 85 has a protein content of 30.41 g/100 g, while the protein content of NIAB Masoor is 30.6 g/100 g, which are outstanding values [13].

The types of lentils commercially available in Hungary are distinguished according to the size and color of the lentil seeds.

In terms of nutrient content, lentils are a protein-rich crop. Comparing the measurement results of several authors, its protein content shows variability. Based on electronic data collection by Ganesan and Bajoun in 2017 from the database of the Department of Agriculture, Agricultural Research Service, Food Data Central operated by the government of the USA, the protein content of lentils is 24.44-25.71 g/100 g [14]. According to the New Nutrient Table (2005) edited by Imre Rodler, the protein content of lentils is 26 g/100 g, its carbohydrate content is 53 g/100 g, and the fat content is 1.9 g/100 g [15].

In 2004, Wang and Daun examined lentil samples grown by several randomly selected Western Canadian producers. The average protein content of the large seed brown lentils examined by them was 27.3 g/100 g, its carbohydrate content was 44 g/100 g, and the fat content was 1.2 g/100 g, while the average protein content of the medium seed brown lentils was 25.9 g/100 g, its carbohydrate content was 44.8 g/100 g, and the fat content was 1.0 g/100 g [16]. The mineral content of lentils measured by other authors is shown in Table 2.

Table 2. Mineral content of lentils (mg/100 g)

3.3. Beans

Among legumes, the most important plants for the food industry belong to the Fabaceae family. These are peas, beans, lentils, lupine and peanuts.

Beans (Phaseolus vulgaris L.) belong to the family of Papilionaceae. They are native land is considered to be the areas of Mexico and Guatemala 500-1,800 m above sea level, and they came to Europe after the discovery of the New World. The oldest bean finds are almost 10,000 years old and were found in Peru [17]. They are characterized by a great richness of form, and there are several variants within the species. Their flowers have a well-developed, zygomorphic, characteristic butterfly shape with bilateral symmetry. The fruit is a multi-seeded, flattened or cylindrical pod. The pods contain 4 to 8 seeds, depending on the variety. The color of the seed is varied.

In Hungary, two species are grown: common beans, also known as garden beans (Phaseolus vulgaris L.), and creeper beans or butter beans (Phaseolus coccineus L.). World bean production (Phaseolus vulgaris L.) was 11.23 million tonnes in 1961 and 30.43 million tonnes in 2018, which means a nearly threefold increase. In 2018, the world’s largest bean-producing country was India with 6.22 million tonnes, followed by Brazil with 2.62 million tonnes. The volume produced in Hungary has decreased significantly in the last 50 years: while in 1962 the amount of beans produced was nearly 31 thousand tonnes, by 1990 this number had decreased to 3,546 tonnes. The low point was 2010 with 545 tonnes. From 2014 to the present, the average production has been 1,500 to 1,700 tonnes/year [3]. The amount of nutrients found in beans depends on the variety, the climate, the growing area and the cultivation technology. Beans can be stored for years under appropriate conditions without damage [18].

In terms of nutrient content, the most valuable component of ripe beans is protein. Bean proteins are made up of valuable essential amino acids such as lysine, methionine, cysteine and tryptophan.

The nutrient and mineral content of beans measured by other authors is shown in Table 3.

Table 3. Nutrient and mineral content of beans from different sources (per 100 g)

3.4. Sulfur-nitrogen ratio

The sulfur content of foods is not very often determined, although its amount is an important indicator of sulfur-containing amino acids. Sulfur occurs I the soil in organic and inorganic forms. The most important sulfides in the soil are FeS2 (pyrite) and FeS, and the most important sulfates are gypsum (CaSO4·2H2O) and anhydrite (CaSO4). The amount of organically bound sulfur varies in direct proportion to and is strongly correlated with the humus content of the soil: r=0.84. The organic sulfur content of the soil varies from soil type to soil type [31]: in chernozem soils it is 75%, while in podzolic soils it is approximately 50%. The sulfur replenishment in different soil types also depends on air pollution and on industrial sulfur emission. Between 1972 and 1974, the amount of sulfur precipitating from the air due to air pollution in the central parts of Great Britain reaches 50 kg/year/ha [38]. In 1980, A. Martin compared the results measured by several authors over a period of 20 years and found that the amount of sulfur precipitating from the air varied by geographical area and season [39]. In 1988, J. Archer calculated the amount of sulfur in agricultural production areas in East England as generally at least 30 kg/year/ha, based on several measurements carried out on the 1970s [36]. In the United Kingdom, sulfur dioxide emissions have been steadily declining for the last 50 years. Emissions today are about 3% of those measured in the 1970s [40]. Plants usually absorb most of the sulfur through the roots in the form of sulfate, or through the stomata of the leaves. The absorbed sulfate is reduced in several steps. It first reacts with ATP to form adenosine phosphosulfate (APS), while inorganic phosphate (Pan) is released from ATP:

SO42- + ATP → APS + Pan

With the help of ATP, APS is phosphorylated a second time to phosphoadenosine phosphosulfate. The sulfate thus bound is reduced to sulfite by an enzyme carrying a hydrogen atom, then it is then further reduced by NADPH to sulfide-S (S2-), which reacts with serine to form cysteine [32].

Sulfur occurs in plants in both inorganic and organic forms. There is no sharp boundary between the two fractions, sulfate is the S reserve of the plant. If the sulfur supply of crops is increased, the inorganic sulfur content will increase primarily, and organically bound sulfur to a lesser extent. The absorbed sulfur is stored by the plant in the form of sulfate, which is reduced to an organic form as needed. First, the plant meets its organic sulfur demand, only then the absorbed sulfur is stored [33]. The greatest significance of sulfur is that it is a constituent of peptides, proteins and lipids, and a building block of sulfur-containing amino acids. Of the sulfur compounds, the amount of cysteine and methionine is significant. The presence of these is essential in various food and feed raw materials. The specific role of sulfur is manifested in enzymes and coenzymes containing the SH group. 90% of SH groups are linked to proteins in plants. In the case of sulfur deficiency, the protein synthesis of the plant is disturbed, the amount of soluble nitrogen compounds increases and the protein content decreases [20]. Then relationship between the elements can be demonstrated by statistical methods. In studies on bread wheat, a correlation of r=0.73 (α=0.01) was measured in the relationship between the sulfur and nitrogen content [21]. In Poland, studies on beans (Phaseolus vulgaris L.) have been carried out for several years, during which the protein content of the crop was increased by 13.6% with adequate sulfur supply [37]. In Northern Germany, in a study on rapeseed, the nitrogen uptake of the plant was increased by 40% with adequate sulfur supply [34].

As no comprehensive studies had been found in the literature by us regarding the composition of the individual crops, we consider it important to provide basic data on this element for the food raw materials studied as well.

4. Materials and methods

4.1. Raw materials

Samples were purchased in December 2020, by random subjective selection in various retail stores in Hungary. The selection criteria was for the samples to differ according to their country of origin or distributor. Seven types of brown lentils from five different distributors and countries of origin, four types of jasmine rice from four different distributors and three countries of origin, and four types of white beans from four different distributors and three countries of origin were analyzed and their nutrient contents were determined. Summary tables of the results, descriptive statistical analyzes and graphs were prepared in Microsoft Excel.

The samples analyzed are listed in Table 4 based on their crop characteristics.

Table 4. Samples analyzed and their characteristics

4.2. Analytical method

Analytical tests were performed on the basis of the food analysis guidelines of the Hungarian Standards Institution (HSI) and the Hungarian Food Codex at the Faculty of Agriculture, Food Science and Environmental Management Instrument Center of the University of Debrecen. Analytical methods are listed in Table 5. To determine the protein content, the amount of nitrogen measured was multiplied by 6.25.

Table 5. Analytical methods

Note: “MSZ” means “Magyar Szabvány = Hungarian Standard”

Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) is a quantitative elemental analysis method, for which samples were prepared according to a study published by professors and lecturers of the University of Debrecen [35].

4.3. Statistical method

Statistical analysis was performed using descriptive statistical analysis, and regression analysis was performed using Microsoft Excel.

5. Results and evaluation

5.1. Results of rice samples and their evaluation

The results of the nutrient analysis of the rice samples are shown in Table 6, and the descriptive statistical evaluation of the data is presented in Table 7. The measurement results and their statistical evaluation of the mineral analysis are summarized in Table 8 and 9, the measurement results demonstrated in Figures 1 and 2.

Table 6. Nutrient content of the rice samples
Table 7. Statistical evaluation of the nutrient content of rice samples
Table 8. Measured mineral content of rice samples and their statistical analysis, Part 1

*Moderately variable

Figure 1. Measured mineral content of rice samples, Part 1
Table 9. Measured mineral content of rice samples and their statistical analysis, Part 1
Figure 2. Mineral content of rice samples and their statistical analysis, Part 2

The protein (6.47-7.04 m/m%), carbohydrate (77.49-78.94 m/m%) and dietary fiber (4.52-4.91 m/m%) content of the rice samples was homogeneous. In the case of the samples tested, the mineral content exhibited moderate or high variability. The highest variability was observed when measuring the Na (CV%=27.19) and Fe (CV%=26.43) content. It is worth noting that the Na content was highest in the sample from Cambodia and lowest in the samples from Thailand, while the Fe content was highest in one of the samples from Thailand and lowest in the sample from Cambodia and another sample from Thailand.

5.1.1. Sulfur-nitrogen ratio

The relative S/N ratios of the rice samples are shown in Table 10.

Table 10. Amount and ratio of sulfur and nitrogen

The fifth row of the table is based on the lowest ratio (R2) and shows the percentage difference from it.

The strongest correlation is found between samples R2 and R4; their country of origin is Thailand, but the final value is also close for sample R1. The largest deviation was found in the case of sample R3, with its country of origin being Vietnam. The correlation indicates the similar agrochemical characteristics of the soil and the cultivation area.

5.2. Results of lentil samples and their evaluation

The results of the nutrient analysis of the lentil samples are shown in Table 11, and the descriptive statistical evaluation of the data is presented in Table 12. The results and statistical evaluation of the mineral analysis are summarized in Tables 13 and 14 and Figures 3 and 4.

Table 11. Nutrient content of the lentil samples
Table 12. Statistical evaluation of the nutrient content of lentil samples
Table 13. Mineral content of lentil samples and their statistical analysis, Part 1

*Moderately variable

Figure 3. Measured mineral content of lentil samples, Part 1
Table 14. Mineral content of lentil samples and their statistical analysis, Part 2

*Moderately variable

Figure 4. Measured mineral content of lentil samples, Part 2

In the case of the samples tested, several values showed moderate variability in terms of mineral content. The protein (19.91-24.05 m/m%), carbohydrate (53.46-56.86 m/m%) and dietary fiber (18.76-20.14 m/m%) content of the lentil samples was found to be statistically homogeneous, but there was a 15% difference between the lowest and highest values in percentage terms. Of minerals, phosphorus (CV%=13.3) and copper (CV%=10.67) exhibited moderate variability. The other minerals were statistically homogeneous. It is important to note that the amounts of Mg, Mn, Na, S and Zn were statistically homogeneous, but the values were in the upper part of the statistical range (CV%~10). The protein, carbohydrate and dietary fiber contents were all homogeneous.

The amount of phosphorus had the highest coefficient of variation. This value was lowest for the samples from Russia and Poland, while it was highest for the produce grown in Ukraine. In general, lentils grown in Canada and Poland had the highest mineral content, while it was lowest in the lentils grown in Russia and Ukraine. The relative S/N ratios of the lentil samples are shown in Table 15.

Table 15. Sulfur-nitrogen ratios of the lentil samples

In the case of medium seed samples (L1, L2, L5, L6, L7), the values for samples L1, L2 and L7 were closest to each other. These samples came from Ukraine and Russia. In the case of samples L4 and L5, the cultivation area was the same, but sample L4 was large seed brown lentils, while sample L5 was medium seed lentils, the values of which were well separated from the values of other cultivation areas. Sample L3 (Canada) was also large seed lentils, with an S/N ratio different from the other values.

5.2.1. Regression analysis of sulfur-nitrogen ratio

Regression analysis of the amount of sulfur and nitrogen was performed only in the case of lentils, given the larger number of samples. Our regression statistics measurement data are shown in Table 16, the line characteristic of the correlation and the equation of the line are shown in Figure 5.

Table 16. Characteristic values of the regression analysis of S-N values (p=0.05)
Figure 5. The line describing the correlation of S and N and its equation

The correlation between sulfur and nitrogen content can also be measured in wheat studies, and the correlation is r=0.7515 [25], which affects the amount of cystine as a gluten component, and thus the quality o the finished product [26].

5.3. Results of dried bean samples

The results of the nutrient analysis of the bean samples are shown in Table 17, and the descriptive statistical evaluation of the data is presented in Table 18. The results of the mineral analysis and their statistical evaluation are summarized in the Tables 19 and 20 and demonstrated in Figures 6 and 7.

Table 17. Nutrient content of beans
Table 18. Statistical evaluation of the nutrient content of bean samples
Table 19. Statistical evaluation of the measured mineral content of bean samples, Part 1

*Moderately variable / **Highly variable

Figure 6. Mineral content of bean samples and their statistical analysis, Part 1
Table 20. Statistical evaluation of the measured mineral content of bean samples, Part 2

*Moderately variable / **Highly variable

Figure 7. Mineral content of bean samples and their statistical analysis, Part 2

The protein (18.8-19.96 m/m%), carbohydrate (57.55-58.14 m/m%) and dietary fiber (23.27-24.33 m/m%) content of the white bean samples was statistically homogeneous, but with the exception of magnesium, the results showed moderate or high variability in terms of the amount of minerals. Moderately variable were the phosphorus (CV%=16.67), sulfur (CV%=15.55), iron (CV%=14.84), manganese (CV%=16.02) and zinc (CV%=19.26). Highly variable were calcium (CV%=27.41), copper (CV%=21.44), potassium (CV%=21.15) and sodium (CV%=22.44).

The highest mineral content was measured in the case of beans grown in Hungary, while the lowest was measured in the case of beans grown in Ethiopia and Ukraine.

5.4. Comparison of the measured values with the reference values

The measured data were compared with the values in the New Nutrient Table edited by Imre Rodler [15]. Percentage differences in the nutrient and mineral contents are shown in Table 21 for rice, Table 22 for lentils and Table 23 for beans.

Table 21. Percentage differences in the nutrient and mineral contents of rice samples

*Results with different orders of magnitude.

The amounts of copper, iron and manganese differ by orders of magnitude from the values of the New Nutrient Table (data highlighted in brick red in Table 21). After comparing the values in the New Nutrient Table with the results in Table 1, measured by other authors (Cu=2.6-9.96 mg/kg, Fe=1.83-31.5 mg/kg and Mn=0.07-10.73 mg/kg), it can be stated that the difference is several orders of magnitude compared to the results found in international literature. Because of these differences, it is necessary and recommended to update available basic data periodically.

In the case of the samples, all samples had a lower protein content than the reference value, while all but one sample had a higher carbohydrate content than the reference value [15]. In terms of minerals, the amount of calcium was significantly higher, while the amounts of potassium, magnesium, sodium, phosphorus and zinc were less than the reference values [15].

Table 22. Percentage of differences from the reference values in the nutrient and mineral contents of lentil samples [15]

In the case of the lentil samples, the protein content was significantly lower, while the carbohydrate content was higher. Of minerals, the amounts of calcium, copper and iron were significantly higher, while the amounts of magnesium and sodium were significantly lower than the reference values [15].

Table 23. Percentage of differences in the nutrient and mineral contents of bean samples

The protein content of the bean samples was on average 13.1% lower, and the carbohydrate content was slightly reduced. Of minerals, the amount of calcium was significantly higher, the amounts of iron, magnesium, zinc and phosphorus were higher, while the amounts of manganese and sodium were lower than the reference values [15].

6. Summary, conclusions

In our measurements, on average, the protein content of the crops was lower and their carbohydrate content was higher than the corresponding reference values [15]. With respect to macronutrients, the change is the same as the change in the nutrient content of crops measured by other authors and associated with the climate change of Earth [22, 23, 24]. Strong variability was measured for several minerals. Based on our measurements, our hypothesis was accepted that the significant diversity of the crops by country of origin is reflected in their nutrient content. In the case of lentils, a correlation was found between the amounts of S and N (r=0.88). The S/N ratios observed were almost the same within countries or for neighboring countries, but were different for samples from different cultivation areas. Comparing the results of our measurements with the data in the New Nutrient Table, orders of magnitude differences were found [15].

Based on our work, it is recommended that the variability of the nutrient and mineral contents is taken into account. Adequate nutrient knowledge of the raw materials is essential for accurate menu planning. Providing adequate nutrition for short- and long-term tasks, or for long-term health and availability, can be of great or even strategic importance to those performing work accompanied by high physical or mental strain (such as those working in law enforcement or members of the armed forces). The nutrients needed for these stresses can be provided by a natural diet, but knowledge and availability of accurate data is also a prerequisite.

It is recommended that changes in nutrient content according to the place of origin are taken into account already in the planning and execution phase of raw material procurement procedures.

7. References

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The nutritional value of rabbit meat when using stinging nettle (Urtica dioica) in the ration of rabbits

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The nutritional value of rabbit meat when using stinging nettle (Urtica dioica) in the ration of rabbits

DOI: https://doi.org/10.52091/EVIK-2022/1-5-ENG

Received: September 2021 – Accepted: December 2021

Authors

1 South Ural State Agrarian University, Troitsk, Russian Federation
2 South Ural State University (national research university), Chelyabinsk, Russian Federation

Keywords

feed ration; stinging nettle; rabbit meat; nutritional value; biochemical indicators.

1. Summary

The article presents the results of studying the influence of the supplementary feeding with stinging nettle hay on the ration balance, biochemical indicators, nutritional value, and keeping quality of rabbit meat. It was established that the replacement of 5% and 25% of coarse fodder with stinging nettle hay resulted in an increase in the content of crude (by 3.5-20.3%), digestible protein (by 4.4-22.8%) and carotene (by 3.3-22.7%) in terms of nutritional value. Growing rabbits with the introduction of a dosage of 5% and 25% of the stinging nettle hay of the nutritional value of coarse fodders was characterized by the least feeds per 10 g of the gain as compared to the content in the traditional ration (1.17 kg of feed units/day). The introduction of 5% of the nettle hay into the rabbit ration as compared to the control group: influenced a decrease in the moisture content (the power of influence of -10,38%, P<0.001), an increase in the content of protein (the power of influence of 34.2%, P<0.01), zinc (the power of influence of 35.6%, P<0.01) and manganese (the power of influence of 34.2%, P<0.01) in the rabbit meat.

2. Introduction

Recently, the production of new improved food products providing a person with complete proteins, essential nutrients, micronutrients and vitamins has become increasingly important worldwide. At the same time, the production of cheap, dietary meat and meat products enriched with vitamins has become very relevant. One of the ways to obtain them is a perpetual modification through adjusting animal rations [1, 2, 3].

Most countries have recently experienced a sharp increase in the rabbit meat production. Great importance is attached to the development of rabbit breeding in Russia as one of the sources of providing the population with dietary meat [4]. Rabbit meat can be compared to chicken meat by its juiciness, softness, taste and digestibility. Rabbit meat is low in fat, connective tissue, cholesterol and sodium salts, it is fine-fibred and highly digestible [5, 6]. One of the possible ways of a perpetual modification of rabbit meat is the introduction of stinging nettle (Urtica dioica) into the ration of rabbits [2].

Nettle as a weedy plant is widespread throughout the European part of Russia, the Caucasus and Western Siberia, and is found in Eastern Siberia, the Far East and Central Asia. Nettle belongs to high-yielding plants, it is a good source for obtaining highly nutritious grass meal containing many nutrients. The chemical composition of grass, hay, and grass meal from stinging nettle is presented in Table 1 [7, 8, 9, 10, 11, 12, 13, 14, 15]. In early spring, nettle contains twice more vitamin C than oranges and lemons, and it contains as much provitamin-A as carrots and has much vitamin K – up to 400 IU/kg. Notably, large quantities of ascorbic acid are contained in fresh leaves and stalks of nettle (up to 269 mg/kg), when nettle is dried, it is destroyed, and its amount decreases markedly [11, 16, 17].

Table 1. The chemical composition and nutritional value of stinging nettle hay fodders

Many authors recommend using young nettle in raw, scalded, or boiled form, in the form of infusions, extracts, hay, grass meal or powders as an additive to the ration of pigs, cattle and poultry to increase their resistance, vitality and productivity, as well as to accumulate vitamin A and mineral elements in processed products [18, 19, 20].

The purpose of the research was to study the influence of the supplementary feeding with the stinging nettle hay on the balanced ration, biochemical indicators, nutritional value, and keeping quality of rabbit meat.

3. Materials and methods

The objects of the research were: fodder base, live animals, and carcasses of rabbits of the Soviet chinchilla breed. This breed is the most widespread and promising in Russia among the combined rabbits, it is characterized by a high plasticity and good adaptability to various climatic and feed conditions [21].

The studies covered 30 rabbits aged from 3 to 6.5 months. 3 groups of animals were formed: control and two experimental groups, 10 animals each. The rabbits of the control group received a ration consisting of oats, wheat bran, carrots, cabbage, cereal-and-legume hay and natural land grass (in the summer months) [22]. 5% of the coarse fodder in terms of nutritional value were replaced with stinging nettle hay for the rabbits of experimental group I, and 25% were replaced for experimental group II.

The rabbits were selected by the principle of pairs of analogues [23, 24], and were kept in group cages in identical conditions. All the animals were clinically healthy. The feeding rations for all the rabbit groups were balanced by all nutrients according to the current standards [25]. To make rations, a comprehensive zootechnical analysis of the used fodder was carried out with the help of the IR-4500 infrared analyzer. The content of basic nutrients in the fodder was determined as follows: nitrogen – by Kjeldahl method, fiber – by Kebenerg and Shtoman method, sugar – by the ebuliostatic method (method for the determination of sugars based on the reduction of copper; Ed.), calcium – by the trilonometric method (complex formation titrimetric method using murexide indicator; Ed.), phosphorus – by the colorimetric method, ash – by the dry ashing method [26].

To prepare nettle hay, young nettle was mowed in May-June and dried in the shade to a moisture content of 12.16%, because rabbits usually do not eat freshly cut nettle [27, 28].

Control weighing of the animals was carried out once a week. The rabbits were slaughtered at the age of 6.5 months after fasting for 24 hours. After stunning, the carcasses were bled white by cutting off the heads. The skins were cased, the extremities were removed along the carpal and tarsal joints, the carcasses were eviscerated and trimmed. The meat was left at a temperature of 15±5 °C for 18 hours for maturation.

When assessing biochemical indicators and nutritional value of the rabbit meat, we determined the content of moisture, fat, protein, and ash, including macronutrients, vitamin C and amino acids. The moisture content was determined in the rabbit meat by drying to a constant weight in an oven at a temperature of 150±2 °C. Meat fat was determined using a Soxhlet extraction apparatus. The amount of protein was determined by mineralization of a meat sample with sulfuric acid according to Kjeldahl, distillation into a solution, followed by titration. The total amount of ash was found by burning organic matter with a free air access. The content of iron, copper, zinc, cobalt, magnesium, manganese and lead in the rabbit meat was determined by dry mineralization followed by atomic absorption spectrophotometry. The content of vitamin C in the meat extract was determined by titration with 2,6-dichlorophenolindophenol. Ion exchange chromatography on an amino acid analyzer was used to examine amino acids in the rabbit meat [29].

The nutritional, energy, and biological value of the studied rabbit meat was calculated according to the generally accepted methods [30, 31].

Studying the keeping quality of the meat when stored for 3 months at –18 °C, we investigated a combination of organoleptic, physico-chemical and microbiological indicators. The amount of volatile fatty acids was determined by distillation of the meat in the presence of sulfuric acid, followed by titration of the distillate with potassium hydroxide. The method for determining ammonia and ammonium salts is based on the ability of ammonia and ammonium salts to form a yellow-brown substance with Nessler’s reagent. The essence of determining the primary protein breakdown products in the broth lies in the deposition of proteins by heating and the formation of copper sulfate complexes with the products of the primary breakdown of the depositing proteins in the filtrate. The acid index characterizing the degree of fat spoilage was found by alkali titration of molten fat [32].

Statistical processing of the research results was carried out according to a regulated method [33] using the Microsoft Excel XP and Statistica 8.0 software suites. The dependencies in the experimental data were searched using the variance analysis [34].

4. Results and discussion

4.1. Studying the rabbit ration balance

All the experimental animals received the same fodder during the experiment (with the exception of nettle hay), taking into account their age and live weight. The rabbits received oats, grass-and legume hay, natural land grass in summer; carrots and cabbage were added to the ration three times a week. The animals of the control group did not receive stinging nettle hay, 5% of the coarse fodder in terms of nutritional value were replaced with the nettle hay for the rabbits of experimental group I, and 25% were replaced for experimental group II. The rations were compiled taking into account the age of the animals – for the animals aged 90-120 days and for the rabbits older than 120 days (Table 2).

The rations of all the experimental rabbits aged 90-120 days were balanced by the main nutrients, except for the high fiber content (1.6-1.7 times more than the norm). The rations of the experimental groups (for 1 animal per day), as opposed to the control group, contained slightly less feed units (-1 and -6 g of feed units*) and, accordingly, less energy value (-0.01 and -0.07 MJ), but significantly more raw protein (+1.2 and +5.4 g per 100 g of feed units) and digestible protein +5.8 and +26.7 g per 100 g of feed units), and carotene (+0.5 and +2.0 mg per 100 g of feed units).

Table 2. The consumption of fodders by the animals during the experiment (day/animal)

* 1 feed unit: energy content of 1kg of medium dried oats

The rations for the older rabbits (1 animal per day), similar to the rations for the young rabbits, were characterized by a high fiber content – by 1.4-1.5 times. The rations of the experimental groups contained more raw protein (+1.2 and +7,0 g per 100 g) and digestible protein (+5.9 and +33.8 g), carotene (+0.5 and +2.6 mg) and slightly less energy value (-0.01 and -0.06 MJ) than in the control group. The increased content of crude and digestible protein, carotene, and vitamin E in the rations of the experimental groups throughout the entire experiment was preconditioned by the addition of the stinging nettle hay rich in these substances.

Note: The two values in parentheses always refer to the two nettle portions: 5% and 25%, respectively.

However, due to the lower energy value of the stinging nettle hay than the grass-and-legume hay, we observed a decrease in the nutrition value in the rations of the experimental groups as compared to the control group.

The ration structure for the rabbits aged 90-120 days contained coarse fodder – 29-31%, succulent fodder – 2-3%, green fodder – 27-28%, concentrates – 39-41%. The ration for the rabbits older than 120 days contained coarse fodder – 32-34%, succulent fodder – 21-22%, concentrates – 45-46%, there was no green fodder.

As it can be seen from the consumption of fodders over the entire experiment, breeding of the rabbits with the introduction of 5% (per 0.13 kg of fed units) and 25% (per 0.05 kg of fed units) of the stinging nettle hay in terms of nutritional value of coarse fodders as compared to the content in the traditional ration was characterized by the lest feeds per 100 g of the gain by feeding 25% nettle.

4.2. Studying the biochemical indicators and nutritional value of rabbit meat

Rabbit meat is close to chicken by its dietary indicators and surpasses it by the content of protein. There is no significant difference in the chemical composition of rabbit meat of different breeds. The chemical composition of meat depends more on the animal age and the feeding level [5, 6].

The content of basic nutrients was determined in the muscle tissue of matured rabbit meat (Table 3).

Table 3. The chemical composition of the muscle tissue of the rabbit meat (¯X±S¯x, n=10)

*P<0,05; **P<0,001

It was established that there was less water in the meat of the animals from experimental group I than in the control group (-10,38%, P<0.001) and experimental group II (by 6.66%, P<0.001). The mass fraction of protein in the rabbit meat of experimental group I is larger than in the rabbit meat of the control group by 0.81% (P<0.05), and experimental group II – by 1.30% (P<0.01). The fat content of the muscle tissue in the rabbits of the control group and experimental group I did not differ significantly, while in experimental group II this indicator was lower than in the control group by 0.4% (P<0.05). The content of vitamin C and ash in all the samples was out of statistical control.

The data of the variance analysis covering the chemical composition of the boneless rabbit meat are presented in Table 4.

Table 4. The influence of the supplementary feeding with the stinging nettle hay on the chemical composition of the muscle tissue of the rabbit meat (n=10)

*P<0.05; **P<0.01; ***P<0.001

It was determined that the introduction of nettle had the maximum influence on the water content; the amount of protein and fat in the muscle tissue of the rabbit meat 2.1 and 3.6 times less depended on the supplementary feeding with nettle feeding than the water content of the meat.

Based on the chemical composition, we calculated the energy value of the rabbit meat ignoring perinephric fat (Table 5).

Table 5. Nutrition value of the rabbit meat ignoring perinephric fat, kJ/100 g

It was revealed that the caloric density of the muscle tissue in the rabbits of the control group and experimental group I differed insignificantly (by +4.187 kJ/g i.e., +0.7%), while the muscles of the rabbits in the control group contained more amount of fat, and experimental group I – more protein. The reduced nutrient value of the muscle tissue of the rabbits of experimental group II (by -20.93 and -25.12 kJ/g i.e., -3.4 and -4.1%) is preconditioned by the low content of protein and fat in the muscles. The increased caloric density of the boneless meat and bone meat in experimental group I (+75.36 kJ/g i.e., +9.6%; +62,80 kJ/g i.e., +10.6%) and experimental group II (+20.93 kJ/g i.e., +2.9%; +12.56 kJ/g i.e., +2.1%) was determined by large deposits of fat on the shoulders and groin.

Note: The two values in parentheses always refer to the two nettle portions: 5% and 25%, respectively.

Based on the aforesaid, it follows that the introduction of 5% of the nettle hay into the rabbit ration resulted in a decrease in the moisture content and an increase in the protein content in the rabbit meat, and the introduction of 25% – ensured a lower fat content of the rabbits’ muscle tissue. The energy value of the rabbit meat increased in proportion to the nettle dosage in the ration due to a larger deposition of fat on the shoulders and groin.

The mineral composition of the rabbit meat samples is shown in Table 6.

Table 6. The mineral composition of the rabbit meat (¯X±S¯x, n=10)

*P<P0,05; **P<0,01

It was established that the meat samples of the rabbits in experimental group I was distinguished by a high content of iron and zinc. There is 1.27 mg/kg more (20.66%) iron in it as compared to the meat of the control rabbits, and 0.83 mg/kg (12.61%) more than in the meat of experimental group II, and it has more zinc by 4.20 mg kg (51.33%; P<0.01) and 1.27 mg/kg (11.41%), respectively. The samples of the rabbit meat from experimental group II contain 2.93 mg/kg (35.83%; P<0.01) more zinc than the control group. The highest copper content was observed in the rabbit meat of experimental group II – by 0.07 mg/kg (48.61%) as compared to the control group, and by 0.04 mg/kg (19.16%) as compared to experimental group I.

The least cobalt content was found in the meat of the rabbits of the experimental groups: in the samples of group II this indicator is less than in the control group by 0.14 mg/kg (32.73%), and in the meat of group I – by 0.03 mg/kg (5.91%).

The proportion of magnesium was the same in all the rabbit meat samples, and the proportion of manganese was 2.2 times higher in the meat of experimental group II (P<0.01), and 0.09 mg/kg more (85.85%; P<0.05) in the meat of experimental group I than in the control group. As compared to the meat of the control animals, the lead content in the rabbit meat of experimental group II decreased by 0.10 mg/kg (19.31%), of experimental group I – by 0.07 mg/kg (13.41%).

The results of the variance analysis covering the mineral composition of the rabbit meat are shown in Table 7.

Table 7. The influence of the supplementary feeding with the stinging nettle hay on the mineral composition of the rabbit meat (n=10)

*P<0,05

We can see from the obtained data that the addition of nettle to a larger extent influenced the content of zinc and manganese. In contrast, the effect of nettle is approximately 4 times less on the content of iron and copper and 5-6 times less – on the amount of cobalt, lead and magnesium.

Thus, the introduction of nettle into the rabbit ration increased the content of zinc, manganese, iron and copper in the meat. Moreover, the content of zinc and iron was higher at a dosage of 5% of the nutritional value of coarse fodder than at a 25% dosage, and the amount of manganese and copper grew with an increase in the concentration of nettle in the ration. There was less cobalt and lead in the rabbit meat proportional to the share of nettle in the fodder.

The biological value of rabbit meat is judged by the content of complete and incomplete proteins and their amino acid composition. With the animals ageing, the content of complete proteins in rabbit meat increases, while the content of incomplete proteins decreases. The meat of animals aged 4-5 months may considered to be most complete [6].

To assess the protein quality, we carried out an amino acid analysis of the rabbit meat, the results of which are shown in Table 8.

Table 8. Amino acid composition of the rabbit meat, g/kg (¯X±S¯x, n=5)

It was determined that the content of such amino acids as threonine, serine, proline, alanine, valine, and lysine in the meat was practically the same. As compared to the control rabbit meat, the meat of the rabbits of experimental group I contained slightly more methionine (+9.77 g/kg i.e., +40.79%), isoleucine (+8.27 g/kg i.e., 7.22 times more), phenylalanine (+13.54 g/kg i.e., 6.37 times more), glutamic acid (+6.84 g/kg i.e., 62.40%), glycine (+0.29 g/kg i.e., +16.23 %) and histidine (+3.08 g/kg i.e., 24.38%). The rabbit meat of experimental group II had a higher amount of the same amino acids as compared to the control group: methionine (+2.1 g/kg i.e., 8.77%), isoleucine (+2.81 g/kg i.e., 3.1 times more), phenylalanine (+6.76 g/kg i.e., 3.68 times more), glutamic acid (+6.03 g/kg i.e., 55.01%), glycine (+0.13 g/kg i.e., 7.39%) and histidine (+7.82 g/kg i.e., 61.91%). The amount of some amino acids varied randomly; both high and low indices were present in the groups. This concerned aspartic acid, tyrosine and leucine, while arginine was found only in one sample from the control group and experimental group I.

Note: The two values in parentheses always refer to the two nettle portions: 5% and 25%, respectively.

The amino acid content in the rabbit meat samples was subjected to the variance analysis (Table 9).

Table 9. The influence of the supplementary feeding with the stinging nettle hay on the amino acid composition of the rabbit meat (n=10)

*P<0.05

Judging by the indicator of the nettle’s power of influence on the amino acid content of meat, the amount of phenylalanine, isoleucine, glutamic acid, tyrosine, leucine, methionine and arginine changed most of all due to feeding with nettle.

As a result of the amino acid analysis, we revealed a tendency of prevailing such essential amino acids as methionine, isoleucine and phenylalanine, as well as non-essential amino acids – glutamic acid and glycine in the meat of the rabbits grown on the ration with the introduction of 5% of nettle of the nutritional value of coarse fodder as compared to the 25% dosage and the control group. The histidine content increased in proportion to the concentration of nettle in the rabbit ration.

4.3. Studying the keeping quality of meat

All the frozen rabbit meat samples corresponded to fresh meat by the organoleptic indicators. The surface of the carcasses had a pink drying crust, the fat tissue was yellowish white, the muscles in the section were slightly moist, leaving slight moisty spots on the filter paper (which is typical of frozen meat), pale pink with a reddish tint. The muscles are dense, elastic, the body hole is typical of fresh rabbit meat, the broth is transparent, and its smell was acceptable.

During the chemical analysis of rabbit freshness, we assessed such indicators as the content of ammonia and ammonium salts, the content of primary protein breakdown products in the broth, the amount of volatile fatty acids (VFA), and the fat acidity value in the adipose tissue.

When determining ammonia and ammonium salts, after adding Nessler’s reagent, the meat extract from all the samples remained transparent and acquired a greenish-yellow color, which corresponded to the requirement of fresh meat. The rabbit meat broth from all the samples remained transparent after the addition of copper sulfate, which indicated the absence of primary protein breakdown products in the meat and, therefore, the meat freshness. The amount of volatile fatty acids (VFA) in the muscle tissue and the fat acidity value of the rabbit meat samples are shown in Table 10.

Table 10. The amount of VFA and the fat acidity value of the rabbits (¯X±S¯x, n=10)

* According to Pronin and Fisenko (2018), **P<0.05

As it can be seen from the above data, the content of VFA in all the rabbit meat samples corresponded to fresh meat, but the differences between the groups were unreliable in terms of this indicator. However, the following tendency was observed: VFA in the meat of experimental group I is 0.22 mg KOH (-6.16%) less, and in experimental II it is 0.23 mg KOH (+3.36%) more than in the meat of the control group. As for the acidity value, the fat of the rabbits from all the groups corresponded to the premium-grade fresh fat. The fat acidity value in the rabbit meat of experimental group I and control group did not differ significantly, while in the rabbit meat of experimental group II this indicator was 0.24 mg KOH (-28.16%, P<0.05) lower than in the control group. The influence of the addition of the stinging nettle hay into the rabbit ration on the amount of VFA and the fat acidity value of the meat is shown in Table 11.

Table 11. The influence of the supplementary feeding with the stinging nettle hay on the rabbit meat freshness indicators (n=10)

*P<0.05

It was established that feeding with nettle did not influence the amount of VFA in the rabbit meat after 3 months storage, and the change in the fat acidity value reliably depended on the supplementary feeding with nettle.

Thus, the introduction of nettle into the rabbit ration had a positive effect on the keeping quality of the rabbit meat when stored for 3 months at a temperature of -18 °C. With an increase in the proportion of nettle in the ration, the rabbits’ fat acidity value decreased, i.e., its food safety is increased. A 5% dosage of the nettle hay in the rabbit ration of the nutritional value of coarse fodder resulted in a slight decrease in VFA in the meat as compared to a 25% dosage of nettle. This allowed us to suggest that the lower dosage of nettle in the ration had a better effect on the safety of the muscle tissue in the rabbit meat than the higher dose.

5. Conclusions

The introduction of the studied dosages of the stinging nettle hay into the ration led to an increase in the content of crude (+3.5 and +20.3%), digestible protein (+4.4 and +22.8%) and carotene (+3.3 and +22.7%). In this case, growing rabbits with a dosage of 5% (per 0.13 kg of feed units) and 25% (per 0.05 kg of feed units) of the stinging nettle hay of the nutritional value of coarse fodders was characterized by the least feeds per 10 g of the gain as compared to the content in the traditional ration (1.17 kg of feed units). The introduction of 5% of the nettle hay into the rabbit ration as compared to the control group: influenced a decrease in the moisture content (the power of effect is -10,38%), an increase in the content of protein (the power of influence of +34.2%), zinc (the power of influence of +35.6%) and manganese (the power of influence of +34.2%) in the rabbit meat; we revealed a tendency of prevailing essential amino acids: methionine, isoleucine, phenylalanine, as well as non-essential amino acids – glutamic acid and glycine in the meat.

The introduction of 25% of the nettle hay into the ration resulted in a lower fat content (the power of effect is -19.7%) and a higher manganese content (the power of effect is +34.2%) in the muscle tissue of rabbits.

We revealed a positive influence of the supplementary feedings with nettle on the keeping quality of meat when stored for 3 months at -18 °C due to slightly smaller amounts of volatile fatty acids (-6.2%) and the fat acidity value (-28.2%) than the control samples.

Note: The two values in parentheses always refer to the two nettle portions: 5% and 25%, respectively.

6. Conflicts of interest

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the content of this paper.

7. Acknowledgement

The work was supported by Act 211 of the Government of the Russian Federation, contract No. 02.A03.21.0011.

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Exploiting the beneficial properties of microalgae for food and feed use

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Exploiting the beneficial properties of microalgae for food and feed use

DOI: https://doi.org/10.52091/EVIK-2021/4-1-ENG

Received: September 2021 – Accepted: November 2021

Authors

1 Hungarian University of Agriculture and Life Sciences, Buda Campus, Institute of Food Science and Technology, Food Science Research Group

Keywords

microalgae, protein, composition, feeding, food use, climate change, carbon footprint

1. Summary

By 2050, 9.8 billion people are projected to live on Earth, which means that we need to double our current food production to keep pace with such a large population increase. In addition, rising greenhouse gas emissions and the associated climate change are placing a significant strain on the planet’s ability to sustain itself. However, in order to increase the quantity of proteins of plant origin, it is necessary to increase crop production areas, harvesting frequencies and the quantity of crops produced. Unfortunately, the optimization of these factors is already very close to the available maximum in the current situation. The developed cultivation systems and maximum utilization of the soil power leads to very serious environmental problems, soil destruction, loss of biodiversity and serious environmental pollution through the transport of the produced plant raw materials.
This poses a serious challenge to food security and further increases the risk of hunger. There is therefore a need for agricultural practices that can lead to the cultivation of food and feed crops that have better sustainability indicators and are more resilient to climate change, which can be used to safely produce health-promoting feeds, as well as novel and value-added foods. Within this group, a particular problem is presented by the protein supply of the population, as currently about one billion people do not have adequate protein intake. However, conventional protein sources are not sufficient to meet growing protein needs.
As mentioned above, food and feed proteins are based on plant proteins. In recent years, a prominent role has been played by the research into alternative proteins and the mapping of their positive and negative properties. Among alternative proteins, special attention has been paid to various yeasts, fungi, bacteria, algae, singe cell proteins (SCPs) and insects. In this paper, we focus on the presentation of algae, particularly microalgae, which are of paramount importance not only because of their significant protein content and favorable amino acid composition, but also because they are also sources of many valuable molecules, such as polyunsaturated fatty acids, pigments, antioxidants, drugs and other biologically active compounds. It is important to learn about microalgae biomass in order to be able to develop innovative health food products.

2. Introduction

By 2050, the population of Earth will grow to nearly 10 billion, an increase of about 25% compared to today’s population. In addition, the significant depletion of our Earth’s water resources also makes it necessary to restructure our diet, as the amount of water needed to produce 1 kg of food is 13,000 liters for cattle, 5,520 liters for chicken, while only 50 liters for peas or lentils. All this means that we can expect a significant increase in the price of foods of animal origin, which will mean that we will have to reduce the proportion of them in our diet significantly.

The different plant protein sources make a positive contribution to the protection of the environment and the fight against climate change due to their more efficient use of water. On the other hand, legumes require 30 to 70% less synthetic fertilizers due to their nitrogen-binding properties, they increase soil power and have a positive effect on soil biology. It is also a known fact that due to nutrient transformation losses, the production of 1 kg of animal protein requires at least 6 to 16 times more cultivable area. In addition, the carbon dioxide footprint of the production of foods, especially of those based on beef, is about 10 times more than that of plant-based foods.

The structure of food consumption in Europe is characterized by a 59% proportion of the daily protein intake coming from protein sources of animal origin (meat, fish, milk), with proteins of plant origin representing only 41% of the total. More than 50% of the latter is wheat protein. As a result, a few cereals (wheat, corn, rice) could become staple foods, leading to geographical homogeneity of foods, dietary monotony and nutritional imbalances, increasing the risk of micronutrient deficiencies, overweightness and chronic obesity, as well as NCDs (Non Communicable Diseases), including cardiovascular diseases, stroke, cancer and diabetes.

Based on all this, it is becoming increasingly important to identify and investigate alternative plant and other protein sources, in addition to the protein sources mentioned above, which can contribute to meeting the protein needs of an increasing human population, as well as to addressing the unbalanced nutritional status of them.

An important group of protein crops are legumes (e.g., dried beans, runner beans, chickpeas, horse beans, lentils, grass peas, black-eyed peas, dried peas, autumn and spring peas) with a high protein content of 20 to 40% on average. Dry leguminous seeds are rich in protein and lysine, but low in sulfur-containing amino acids. At the same time, field crops with good nutritional values but low protein content (e.g., sunflower, canola, corn, sorghum, rice, wheat) are low in lysine and rich in sulfur-containing amino acids. Taking into account the positive nutritional values of the two plant groups, products containing complete plant protein can be developed by their combined use.

Table 1. Protein content of various crops [1]

The protein content of various crops (Table 1) may show significant variability not only between species but also within a given species. In addition, protein content can also be altered by environmental factors and the food processing technology.

Other alternative protein sources include single cell proteins (SCPs) produced by fermentation technologies, seaweeds living in saltwater, duckweed species living in freshwater and various insect species. Protein content values of the different sources can vary widely depending on the species, the cultivation technology and the nutrient supply (Table 2).

Table 2. Protein content of various alternative protein sources [1]

3. Characterization and occurrence of microalgae

Algae, also called seaweeds, are eukaryotes capable of photosynthesis. Algae represent one of the oldest life forms on Earth, having existed on our planet for about 3 billion years. They produce one-third of Earth’s living matter and about 50% of its organic carbon [1]. These plants have survived all geological epochs and climate changes. Algae still account for 90% of Earth’s oxygen production. These organisms have allowed life to form on Earth, and they use the power of sunlight to produce organic food from inorganic materials through photosynthesis. In many respects, algae are the most diverse living things in the world. They have the simplest structure and are closely related to bacteria. The most complex ones, Charophyceae species, resemble kelp to the point of confusion. The smallest algae are picoalgae with a size of 0.5 μm, while the largest are 50 to 100 m long Macrocystis species (Phaeophyceae). They occur under the most extreme conditions in fresh and salt water, hot springs, on snow and ice surfaces, in the soil and in the upper layer of some rocks [2]. Algae are mostly eukaryotes, typically classified as „lower” plants that have no true stems, roots and leaves, and are generally capable of photosynthesis. Algae are widely classified into Rhodophyta (red algae), Phaeophyta (brown algae) and Chlorophyta (green algae) and are classified as macroalgae and microalgae by size. Macroalgae (seaweeds) are multicellular, large size algae that are visible to the naked eye, while microalgae are microscopic single-celled organisms and may be prokaryotes, similar to cyanobacteria (Chloroxybacterium), or eukaryotes, similar to green algae (Chlorophyta).

Microalgae, as excellent sources of various organic carbon compounds, can be used in the manufacture of health supplements, drugs and cosmetics [2]. they can also be used in wastewater treatment, atmospheric CO2 reduction and the production of biofuels. A wide range of bio-products can be extracted from microalgae, such as polysaccharides, lipids, pigments, proteins, vitamins, bioactive compounds and antioxidants [3]. In addition to all this, they are playing an increasingly important role in the feed and food industries (Figure 1).

Figure 1. Applications of microalgae [3]

4. General composition of microalgae

As with all other higher plants, the chemical composition of algae varies depending on the type of cultivation, such as environmental parameters, temperature, illumination, pH and mineral content of the medium, CO2 supply and mixing speed: 9-77% protein, 6-54% carbohydrate, 4-74% lipid (Table 3)

Table 3. Comparison of the protein, carbohydrate and fat content of some food ingredients and microalgae

4.1. Protein and amino acid content of microalgae

Based on research results it can be stated that algae are a source of protein with an amino acid composition similar to that of plant proteins. Examination of the net protein utilization, i.e., of the proper amino acid composition, digestibility and biological value, also led to a similar result.

Several microalgae species produce large amounts of various essential amino acids and proteins that can be used in foods and feeds, one of the main reasons they occupy a prominent place among alternative proteins. Certain species of microalgae can produce as much protein as other rich protein sources, e.g., eggs, meat and milk [6].

In addition, the amino acid pattern of almost all algal species is very similar to the protein pattern of many foods. Of the amino acids, they are only low in cysteine and lysine. Since cells are able to synthesize almost all amino acids, they can be used to provide the essential amino acid needs of both humans and animals [7]. The composition of amino acids synthesized by microalgae, especially the amount and composition of free amino acids, varies greatly, depending on the species, growth conditions and the growth phase [8].

In addition, microalgae proteins are easily digestible and have a relatively high nutritional value. Microalgae produce 2.5 to 7.5 tons/ha/year protein [9], for example, the green microalgae Chlorella is a rich source of different types of marketed proteins. Another protein-rich microalga is Arthrospira. Proteins from microalgae lower cholesterol levels by activating cholecystokinin. They also have other important enzymatic effect [10]. For example, the microalga called Lyngbya majuscula produces microcholine-A, a protein with immunosuppressive effects [11]. The microalga Nostoc produces a protein called cyanovirin, which is known to have an antiviral activity against both HIV and the influenza virus [12]. At the same time, Anabaena and Porphyridium species produce the enzyme SOD (superoxide dismutase), which protects against oxidative damage, while Isochrysis galbana produces the enzyme carbonic anhydrase, which plays a key role in the conversion of CO2 to carbonic acid and bicarbonate. Microcystis aeruginosa produces a number of amino acids, including proline, serine, glycine and valine.

4.2. Fatty acids

Polyunsaturated fatty acids play an important role in tissue protection and have a beneficial effect on health. Omega-3 and omega-6 fatty acids are especially important for humans, but the human body is unable to produce these fatty acids. Therefore, intake from an external source, such as various foods, is essential. Docosahexaenoic acid (DHA), linoleic acid, eicosapentaenoic acid (EPA), arachidonic acid and gamma-linolenic acid have been shown to lower cholesterol levels, delay aging, protect membrane integrity and prevent cardiovascular diseases [13,14]. Several species of microalgae capable of synthesizing these valuable fatty acids have been studied. These studies have shown that Pavlova lutheri produces large amounts of polyunsaturated fatty acids [15], Arthrospira platensis mainly produces and accumulates stigmasterol, sitosterol and γ-linolenic acid [16], while Porphyridium produces arachidonic acid, Nannochloropsis, Phaeodactylum, Nitzschia, Isochrysis and Diacronema species produce eicosapentaenoic acid and Crypthecodinium and Schizochytrim microalgae produce docosahexaenoic acid in significant amounts [17, 18, 19].

The polyunsaturated EPA and DHA are also pharmaceutically very important omega-3 fatty acids. They play a key role in the treatment of inflammatory diseases, heart problems, arthritis, asthma and headaches, among other things [20, 21, 22].

4.3. Polysaccharides

Polysaccharides are widely used in the food industry, primarily as gelling and thickening agents. Many polysaccharides used in the food industry, such as agar, alginates and carrageenans are extracted from macroalgae, e.g., Laminaria, Gracilaria and Macrocystis species [8]. One of the most promising microalgal species, the unicellular red alga Porphyridium cruentum produces a galactan exopolysaccharide, that can replace carrageenan in many cases. Chlamydomonas mexicana also produces significant amounts of polysaccharides, and it is used in the United States as a soil improver. Sulfated algal polysaccharides also have pharmacological properties and they play a prominent role in stimulating the human immune system [23].

4.4. Photosynthetic pigments

It can be said in general that each species of algae has a specific pigment combination that creates its characteristic colour. In addition to chlorophylls, the primary photosynthetic pigments, supplementary or secondary pigments, such as phycobilins and a number of carotenoids are also produced by microalgae. These natural pigments have the ability to improve the efficiency of light energy utilization and provide protection for algae from the harmful effects of solar radiation. They are used preferentially when added to foods and feeds as natural antioxidants and colourants [24].

4.4.1. Carotenoids

Carotenoids are naturally occurring pigments that play a role in the formation of the colour of fruits, vegetables and other plants [25]. They are typically isoprenoid polyene pigments derived from lycopene, ranging in colour from yellow to red, and are produced by de novo photosynthetic organisms and some other microorganisms [8]. Carotenoids ingested with foods or feeds are either accumulated or metabolized by the body. Carotenoids can be found in the meat of various animals, eggs, fish skin (trout, salmon), crustaceans (shrimp, lobster, Antarctic krill, crab) and subcutaneous fat, skin, egg yolk, liver and the feather of birds (e.g., poultry) [26].

In algae, carotenoids primarily play the role of sunscreen and light collector, i.e., they protect the photosynthetic apparatus from light damage [24]. They also play a role in phototropism and phototaxis. Some microalgae undergo carotenogenesis in response to various environmental effects (e.g., light, temperature, salts, nutrients). During this, algae stop their growth dramatically alter their carotenoid metabolism, resulting in the accumulation of secondary carotenoids [27].

There are more than 600 carotenoids in nature, about 50 of which show provitamin A activity. These include α-carotene, β-carotene and β-cryptoxanthin [28]. A β-Carotene protects membrane lipids from peroxidation, thus preventing and reducing the development of many serious and fatal diseases, such as cancer, cardiovascular disease, Parkinson’s disease and atherosclerosis [29, 30, 31].

Relatively few carotenoids are used in the food and feed industries: β-carotene and astaxanthin, lutein, zeaxanthin, lycopene, etc. Among microalgae, the main carotenoid-producing species are Dunaliella salina and Haematococcus pluvialis which produce significant amounts of β-carotene and astaxanthin, respectively. The microalga Dunaliella salina produces β-carotene in an amount that accounts for 10 to 14% of its dry matter content [32].

β-Carotene serves as an essential nutrient, mainly as a food colouring, and is increasingly used in various dietary supplements due to its health-protective effects, but is also used preferentially by the cosmetics industry [33].

In the food industry, β-carotene is also used regularly in various soft drinks, cheeses, butter or margarines due to its beneficial physiological effect, as it possesses a provitamin activity [34].

Astaxanthin has a number of beneficial properties, including improving eye health, muscle strength and endurance, protecting the skin, reducing premature aging, inflammation and damage caused by UVA radiation. It also plays an important role in animal feeding, as it promotes growth and reproduction, improves vision, has an immunostimulatory effect and also aids in post-injury regeneration [35, 36].

Numerous studies have shown that daily intake of astaxanthin protects cells and tissues from oxidative effects and that its effect on free radicals is significantly, about 500 times more intense than that of vitamin E. The microalga Haematococcus pluvialis produces 4-5% astaxanthin on the basis of dry biomass [37], so its dried biomass is marketed as a rich source of astaxanthin and is sold on the market at a price of about 2,500 US $/kg.

4.4.2. Chlorophyll

Each alga contains one or more chlorophylls. Their primary photosynthetic pigment is chlorophyll-a, and it is also the only chlorophyll in Cyanobacteria (blue-green algae) and red algae (Rhodophyta). Like all higher plants, Chlorophytes (true green seaweeds) and Euglenophytes (flagellate seaweeds) also contain chlorophyll-b; chlorophyll-c, -d and -e are found in many other marine algae. The quantity of chlorophylls is usually up to 0.5-1.5% of the dry matter content [38].

In addition to being used as a food and pharmaceutical colourant, chlorophyll derivatives also have a health-protective effect. They are also traditionally used because of their wound healing and anti-inflammatory properties [39]. Epidemiological studies conducted in the Netherlands (Cohort Study) have shown a significant correlation between chlorophyll consumption and a reduction in the risk of colon cancer [40].

4.4.3. Phycobilins

In addition to chlorophyll and carotenoid lipophilic pigments, Cyanobacteria (blue-green algae), Rhodophytes (red algae) and Cryptophyta algae also contain so-called phycobilins, which are coloured, fluorescent pigments. Like chlorophylls, they bind to proteins (phycobiliproteins) which, contrary to chlorophyll-protein complexes located in the membranes, are water-soluble proteins and are important components of the photochemical system. Significant amounts of phycobilins, mainly blue phycocyanin and red phycoerithrin are found in Spirulina algae and Porphyridium, respectively.

The use of phycobilins is quite widespread. In addition to being widely used in clinical immunofluorescence studies to detect fluorescently labeled antibodies as a fluorescent marker [38], phycocyanin is currently used in Japan and China as a natural colourant in foods, such as chewing gums, candies, dairy products, jellies, ice creams and soft drinks. It is also widely used in the cosmetics industry, for example, in lipsticks, eyeliners and eyeshadows [41].

According to a study, phycocyanin is one of the most versatile blue colourant, providing a bright blue colour to various jellies and coated soft candies [42], while a number of pharmacological properties are also attributed to phycocyanin, including antioxidant, anti-inflammatory, neuroprotective and hepatoprotective effects [43, 44, 45].

4.5. Tocopherols and sterols

Tocopherols are widespread in nature, occurring in both lower and higher plants as parts of the photosynthetic system.

Research in this area has revealed that Euglena has the highest content of tocopherols among the various microalgae species [46].

The sterols produced by plants are called phytosterols. Microalgae can make a major contribution to the production of phytosterols, they can be considered as efficient and promising sources for their large-scale production. Some microalgae contain high levels of sterols. Microalgae sterols have health-protective, cholesterol lowering and anti-inflammatory properties, and are effective in the treatment of certain neurological diseases, such as Parkinson’s disease [47, 48], and are increasingly used in the food industry as dietary supplements and food ingredients [49, 50]. Some microalgae, such as species belonging to the genera Pavlova and Thalassiosira, are rich in sterols [51, 52].

4.6. Vitamins, minerals

Microalgae biomass is a valuable source of almost all essential vitamins, as it contains vitamins B1, B2, B3, B5, B6, B12, C, E and H, among other things, and its mineral content (e.g., Na, K, Ca, Mg, Fe, Zn and trace elements) is also significant [53]. The vitamin B12 and iron content of some microalgae, such as Spirulina species, is particularly high, therefore, they are often used in foods and dietary supplements made for vegetarians.

The vitamin content of algae depends on the genotype, as well as the stage of the growth cycle, the nutrition of the algae and the light intensity. Thus, their vitamin content can be increased by selecting the right species, choosing the right culture conditions and/or by genetically modifying them. However, the vitamin content of the cells can be significantly reduced by using inappropriate environmental conditions, harvesting and biomass drying methods [54].

4.7. Antioxidants

Microalgae are photoautotrophic organisms, that is, organisms that depend on light as the energy source to produce organic molecules from inorganic molecules. This process is known as photosynthesis, and the food chain is usually based on these organisms. During their development, these organisms have developed an effective defense system against various abiotic effects affecting them, such as high levels of free radicals and reactive oxygen species [23]. Due to the high antioxidant content of certain algae species (e.g., Isochrysis galbana, Chlorella vulgaris, Nannochloropsis oculata, Tetraselmis tetrathele, Chaetoceros calcitrans), their use has been increasing in some cosmetics (e.g., sunscreens) and in functional foods.

The research of Natrah et al. [55] has shown that the methanolic extract of some fresh/untreated microalgae exhibits an antioxidant activity that is higher than that of α-tocopherol, but lower than that of the synthetic antioxidant BHT (butylhydroxytoluene). However, the latter and BHA (butylhydroxyanisole) being synthetic antioxidants, their safe use if questionable, as their use in high doses may be carcinogenic and tumorigenic [56, 57].

4.8. Other biologically active components

Microalgae are undoubtedly a large repository of versatile compounds with significant biological activity, as well as unique and interesting structure and function [58].

In recent decades, marine microorganisms, especially Cyanobacteria, have become the focus of medical research aimed at developing new drugs and antibiotics. Data published up to 1996 revealed about 208 Cyanobacterial compounds exhibiting biological activity. By 2001, the number rose to 424. The compounds identified include various lipoproteins (40%), alkaloids, amides, etc. [59], many of which have cytotoxic, antitumor, antimicrobial (antibacterial, antifungal), antiviral (e.g., anti-HIV) activities, as well as biomodulatory, for example, immunosuppressive and anti-inflammatory effects [59, 60].

Numerous studies have shown that microalgae may also contain compounds that are effective in treating cancer and tumors by inhibiting angiogenesis. Angiogenesis is a physiological process during which new blood vessels emerge from existing blood vessels. Although angiogenesis is a normal process, pathological conditions can develop under certain conditions, such as cancer, atherosclerosis, arthritis, diabetic retinopathy and ischemic stroke. Pathological angiogenesis promotes the development and growth of tumors [61, 62]. Fucoxanthin and fucoxanthinol, found in many species of microalgae, have been shown to inhibit the process of angiogenesis in the aortic ring of rats by reducing the formation and growth of microvessels [63]. Fucoxanthin has also been shown to protect DNA from photooxidation [64]. Microalgae, especially blue-green algae, are currently considered to be potential sources of active ingredients that can be used in the treatment of cancer, as several studies have demonstrated their anti-cancer effects [65].

5. Some major microalgal species

Although many indigenous microalgal populations have been used for various purposes for centuries, their large-scale cultivation has only begun in the last few decades [66]. Of the assumed roughly 30,000 species of microalgae, only a few thousands are kept in stock collections [67, 68], of which a few hundred are considered more important due to their chemical composition, and very few are grown in industrial quantities [69].

The biotechnologically most relevant microalgae include green algae (Chlorophyta), such as Chlorella vulgaris, Haematococcus pluvialis, Dunaliella salina and Spirulina maxima, which belongs to the phylum of Cyanobacteria. Their cultivation, marketing and use are very significant, mainly as dietary supplements and animal feed additives.

5.1. Spirulina species

Spirulina (Arthrosphira) algae are a tiny, filamentous, freshwater, spiral-shaped, blue-green algae species that is abundant in the alkaline lakes of Mexico and Africa and has been consumed by the local population since ancient times [59]. Its characteristic feature is that its cell membrane is very weak, and this makes it easy to utilize. It is also one of its important physiological features that it can easily become a colloidal solution when exposed to moisture and is very easy to digest. Spirulina is widely grown all over the world (3,000 tons/year) and is used as a food and feed supplement due to its high protein content (60 to 70%, including 18 amino acids, 8 of which are essential) and its excellent nutritional value. For example, its γ-linoleic acid content is remarkably high [70, 71]. Its digestibility and absorption are superior to both animal and plant proteins. It contains the vitamins important to the body (C, B1, B2, B5, B6, B9, B12, A, E), trace elements, of which iron, iodine, calcium, sodium, potassium, copper, magnesium, manganese, zinc, phosphorus, selenium, chromium and vanadium are the most significant. It is a particularly good source of iodine and potassium. It stimulates the immune system greatly due to its high content of β-carotene, chlorophyll and γ-linolenic acid. Its polyunsaturated fatty acid content is significantly higher than that of marine fish. Spirulina contains high levels of GLA (gamma-linolenic acid), which is found in greater amounts only in breast milk. Spirulina has a number of health-protecting effects: it lowers high blood fat levels, cholesterol levels, high blood pressure, elevated blood sugar levels, is suitable for treating kidney failure, and promotes the growth of probiotics, such as Lactobacilli, in the gut [19]. It is the main source of natural phycocyanin, used as a natural blue colourant in foods and cosmetic products, and also as a biochemical tracer in immunoassays [70, 71, 72].

5.2. Chlorella vulgaris

This species of algae is one of the oldest, simplest plants on Earth. Its nearly 4% chlorophyll content, strong cell wall and high pigment and cellulose content make the detoxifying effect of Chlorella unique. It binds and removes heavy metals from the body, cleanses the intestinal flora. By improving liver function, it helps to remove other contaminants, in addition to heavy metals, and to detoxify the body.

Its physiological effects are similar to those of Spirulina: it is high in protein, contains all the essential amino acids, and it is a storehouse of various vitamins, trace elements and minerals. Chlorella vulgaris has been used in the Far East since ancient times in alternative medicine, as well as in the preparation of various traditional foods. It is widely cultivated and used, primarily in animal feeding, aquaculture and as a dietary supplement, in many countries, including China, Japan, Europe and the United States. The health-protecting effects of Chlorella are manifested, for example, in the rapid healing of stomach ulcers and other wounds, and it is useful in the treatment of constipation, anemia, hypertension, diabetes, infant malnutrition and neurosis. The prophylactic role of the glycolipids found in Chlorella against the development of atherosclerosis and hypocholesterolemia has been demonstrated by research [58]. However, one of the most important substances in Chlorella is ß-1,3-glucan, which is an active immunostimulant, it binds free radicals and reduces the amount of blood fats [19].

The γ-linolenic acid (GLA) content of Spirulina and Chlorella is very high. The role of GLA in the functioning of the body is extremely diverse. On the one hand, it is important for the proper functioning of the immune system, and on the other hand, it has an anti-inflammatory effect, lowers blood pressure and improves blood circulation. It prevents platelets from sticking together, thus reducing the risk of formation of blood clots. It has a positive effect on cholesterol levels, thus reducing the risk of atherosclerosis. It improves nervous system function and eliminates excess fluid from the body.

5.3. Haematococcus pluvialis

This freshwater microalgae, with a size of barely 0.1 mm, attracted the interest of researchers early on. Haematococcus pluvialis is the plant with the highest astaxanthin content (1.5-3.0% dry weight) based on previous research. This carotenoid pigment has a very strong radical scavenging effect that exceeds the antioxidant properties of β-carotene, vitamin C and vitamin E. The astaxanthin production of the algae is a natural reaction to environmental stress. Thanks to the protective functions of astaxanthin, in a state of deep sleep, these algae can survive without food and water for more than 40 years, so they can easily survive the heat of summer or the icy cold of winter. They will only wake up again and regain their original green, active state when living conditions are right again. As a result, algae defied the harshest environmental conditions even at the early stages of Earth’s history. The ability of certain algae species to survive both droughts and ice ages is due to their astaxanthin shield. Astaxanthin is a bioactive antioxidant that has been shown to be effective against Alzheimer’s disease and Parkinson’s disease, as well as macular degeneration in both animal and human experiments. In some cosmetics, the astaxanthin used can slow down the aging process of the skin. In addition, the immune-boosting and anti-inflammatory effects of astaxanthin have been reported, as well as its beneficial effects on the development of cardiovascular diseases and atherosclerosis.

Haematococcus pluvialis is currently a natural source of this pigment, its commercial utilization is outstanding, especially in aquaculture (salmon and trout farming) [73]. There is another natural source of astaxanthin, however, the yeast Xanthophyllomyces dendrorhous requires large amounts of expensive nutrients for proper pigmentation [36].

5.4. Dunaliella salina

Dunaliella salina is a halotolerant microalgae, its natural habitats being salt lakes. It is able to accumulate large amounts of β-carotene, which is why this species of algae is sought after mainly as a food colourant. Research has shown that the Dunaliella salina community in Pink Lake, Victoria, Australia, can produce up to 14% carotenoids [74] and some Dunaliella algae can contain up to 10% in cultivated cultures.

Higher β-carotene content can be achieved with adequate nutrient supply under high salt and light conditions [75, 76]. Similarly to Haematococcus algae, Dunaliella contains significant amounts of astaxanthin. However, Haematococcus is a freshwater algae that is difficult to grow in outdoor cultures because it is easily infected, requiring a closed system, and the extraction of astaxanthin is more complicated than in the case of Dunaliella, as Haematococcus has a thick cell wall that has to be disrupted by physical methods.

6. Use for animal feed

Today, many species of microalgae (e.g., Chlorella, Tetraselmis, Spirulina, Nannochloropsis, Nitzchia, Navicula, Chaetoceros, Scenedesmus, Haematococcus, Crypthecodinium) are used to feed farm animals, pets and fish.

Even a small amount of microalgae biomass has an immunostimulatory effect, which results in growth stimulation, disease resistance, has antiviral and antibacterial effects, improves absorption and the colonization stimulation of probiotic cultures such as Lactobacilli, and thus results in an increase in reproductive performance and weight [77]. By providing feeds that contain algae, the appearance of the animals improves visibly, which is manifested in a healthy skin and a shiny coat, both in the case of farm animals (poultry, cows, breeding bulls) and in the case of pets (cats, dogs, rabbits, ornamental fish and birds) [78].

Feed is the main exogenous factor influencing animal health and accounts for a significant part of the major cost of animal husbandry, and so it is very important to identify high quality, chemical and toxic substance free alternative protein sources that can replace or complement traditional protein sources [26]. The results of a large number of nutritional and toxicological evaluations have demonstrated the suitability of algal biomass as a valuable feed supplement [38]. Currently, about 30% of the global algae production is sold for feed purposes [53].

Becker et al [53] performed feeding experiments on broiler chickens, in which conventional proteins were replaced with species of different microalgae, namely Chlorella, Euglena, Oocystis, Scenedesmus, Spirulina, usually at a rate of 10%. In laying hens, no differences were found in egg production and egg quality (size, weight, shell thickness, egg solids, albumin index, etc.) and in feed conversion efficiency between birds fed with algae-containing feeds and the control birds.

However, Haematococcus microalgae can also be used as a natural colourant in the feeding of broiler chickens, making the skin of the birds yellower and the egg yolk more orange [79]. Studies were performed on chickens that were fed the biomass (5% or 10%) of red microalgae (Porphyridium species). Although no difference was found in the body weight and egg count of the chickens, the composition of the meat and eggs showed decreased cholesterol levels (by 10%) and healthier fatty acid composition and increased linoleic acid and arachidonic acid levels (by 29% and 24%, respectively). In addition, the colour of the egg yolk was darker due to a carotenoid content that was 2.4 times higher than average [80]. At the same time, it was observed that chickens fed with algal biomass consumed 10% less food in the case of feeds containing either 5% or 10% algae, and had serum cholesterol levels significantly lower (by 11% and 28%, respectively) than that of the control group.

Microalgae biomass is a feed with excellent nutritional values and is eminently suitable for breeding pigs. They can be used to replace traditional proteins such as soy flour or fish meal, and their acceptance is not difficult for the animals [38].

It is hypothesized that algae may be an excellent food source for ruminants, as these animals are able to digest even the cell wall of unprocessed algae. However, only a limited number of experiments have been performed with these animal species, as these procedures are expensive and large amounts of algae are required to perform appropriate feeding experiments. However, some experiments have shown that sheep, lambs and cattle were unable to digest the carbohydrate fraction efficiently when fed certain algal species (e.g., Chlorella, Scenedesmus obliquus and Scenedesmus quadricauda) [81, 82]. Better digestibility was achieved when Spirulina accounted for 20% of the total sheep feed, and it was observed that in calves fed a diet containing Scenedesmus algae, there were only minimal differences when compared to animals fed the control feed [83].

Microalgae feeds are currently used mainly to supplement and replace zooplankton used for the breeding of fish, fish fry and other aquatic animals (crustaceans, etc.) [84, 85]. The species most often used in aquaculture are Chlorella, Tetraselmis, Isochrysis, Pavlova, Phaeodactylum, Chaetoceros, Nannochloropsis, Skeletonema and Thalassiosira [86, 87].

Microalgae contain nutrients that are essential for aquatic animals, and these determine the quality, growth, health and disease resistance of the farmed animals. Mixed microalgae cultures have been shown to be useful for animal growth to provide adequate protein composition, vitamin content and high levels of polyunsaturated fatty acids (mainly EPA, AA and DHA), which are vital for the survival and growth of many freshwater and marine animals in the early stages of life [88]. One of the beneficial effects of algae is attributed to the fact that they increase the food intake of marine fish offspring, which enhances their growth and survival, and also improves the quality of fish meat [89]. In addition, the presence of algae in the breeding tanks of European sea bass larvae has been shown to increase digestive enzyme secretion [90]. When many aquatic species, such as Salmonidae (salmon and trout), shrimp, lobster, marine vertebrates, goldfish and koi carp are kept under intensive conditions, carotenoid colourants are added to their feed to achieve their characteristic muscle colour. Carotenoids, such as astaxanthin and canthaxanthin, have beneficial effects on animal health, growth and reproduction, by promoting the development of larvae [33].

7. Food use

In the early 1950s, microalgae were used to replace certain foods and were often used as single cell proteins in the diets of malnourished children and adults. Today, in human nutrition, microalgae are marketed in the form of various dietary supplement pills, capsules and liquids [91].

Gross et al conducted research in which Scenedesmus obliquus algae was added to the normal diet of children (5 g/day) and adults (10 g/day) during a four-week test period. Blood panel, urine composition, serum protein and uric acid concentration and weight change were measured, but the parameters analyzed did not deviate from normal values, only a slight increase in body weight was observed.

The same authors subsequently performed a three-week study in both slightly (Group I) and severely (Group II) malnourished four-year-old children. Four-year-old children in Group I (10 g algae/day) showed a significant weight gain (27 g/day) compared to children in the control group who received a normal diet, and no adverse symptoms were experienced. Group II was fed a diet enriched with 0.87 g algae/kg body weight, replacing only 8% of the total protein with algae protein, and the daily weight gain was approximately seven times that of children in the control group, while all anthropogenic parameters were normal. The authors concluded that the significant improvement in health can be attributed not only to algae protein but also to other important health-protective and immune-enhancing components [92].

Large-scale production of microalgae suitable for human consumption has been growing worldwide. There are many forms of microalgae and other health-protective dietary supplements on the market, such as various pills, powders, capsules, lozenges and liquids [23, 93]. Microalgae are also used in the preparation of various foods, such as algae pastries, biscuits, breads, snacks, candies, yogurts and soft drinks, which also provide the health and immunomodulatory effects associated with microalgae biomass [94].

Despite some reluctance on the part of consumers over novel foods in recent decades, there is a growing consumer demand for natural, health-enhancing foods today. Thus, functional foods containing microalgae biomass are also becoming increasingly popular. These products are also proving to be very attractive and varied from a sensory point of view, and their consumption also brings health benefits, satisfying consumer needs in all respects [23].

8. Acknowledgement

We are grateful for the funding of the NKTH, TKP2020-NKA 24 "Thematic Excellence Program”.

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Examination of breads enriched with dried basil and evaluation of the results

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Examination of breads enriched with dried basil and evaluation of the results

DOI: https://doi.org/10.52091/EVIK-2021/4-3-ENG

Received: May 2021 – Accepted: September 2021

Authors

1 University of Debrecen, Institute of Food Science

Keywords

bread, basil, total polyphenol content (TPC), flavonoid content, element content, functional food

1. Summary

Both bread and spices play an important role in our daily diet. Basil is an extremely popular spice, the beneficial effects of which have long been known. This is why the enrichment of breads with commercially available dried basil was carried out. In the case of basil, its antioxidant and element contents were determined. With respect to these parameters, results indicating outstandingly advantageous properties were obtained. During the enrichment, 6 different concentrations were used and a control sample was prepared that did not contain basil. As the amount of spice was increased, the total polyphenol content (TPC), flavonoid and macronutrient contents of the breads also increased. There was no difference between the products in terms of their crude fat content. In the case of the protein content, a minimal increase was measured with increasing spice concentration.

2. Introduction

Basil is mostly grown in Mediterranean countries. Its leaves, fresh or dried, are used as a seasoning. It is also known as an herb, its use is recommended against headaches, coughs, diarrhea, constipation, warts, worms and kidney problems, among other things [1]. Rosmarinic acid is responsible for its antioxidant effect, as it binds free radicals. It cannot be used against fungi, but its antibacterial and antiviral activities are known [2]. It has outstanding values in terms of element content, which has been studied by Ghanjaoui et al. [1], Özcan and Akbulut [3], as well as Özcan [4], among others.

Baking is one of the oldest human occupations related to food preparation. When prehistoric man settled down and switched to gathering and farming, cereals became the most important sources of food. With continuous learning and technological development, it has been possible to process the collected seeds into various products [5]. One such product was bread, which was very different from the product that is being consumed today. Nevertheless, many types of bread are still made today. In the Middle East, flat bread dominates, in China, steamed bread, while in America, corn-based product dominate. Bread is mostly made from wheat and some other commonly used cereals, as the proteins of these are best suited to make the right product [6].

According to papers in the literature, there have been several attempts to enrich bread with different substances. Raba et al. [7] prepared and tested bread enriched with garlic and basil. Suleria et al. [8] used aqueous garlic extract, but breads enriched with yellow pepper flour [9], ginger powder [10], turmeric [11], waste onion powder [12], brown algae powder [13], horseradish leaf powder [14], raspberry and strawberry oil cake [15], as well as garlic and its preparations [16] have also been made.

Since, to the best of our knowledge, there have been few bread fortification experiments with spices and then few studies were carried out, we first used basil in our experiment and examined what measurable changes were caused in the baked product by the addition of the spice.

3. Materials and methods

3.1. Bread preparation

In our experiments, different parameters of 7 bread samples were examined. The ingredients were selected and the breads were prepared according to the method of Kántor et al. [16]. The ingredients used to make the products were purchased at retail stores. Basil was added to the bread dough before kneading in the amounts of 0.00 g; 4.25 g; 8.5 g; 12.75 g; 17.0 g; 21.25 g and 25.5 g (Table 1).

Table 1. The names of the breads tested and their basil content

During the experiment, the total polyphenol (TPC), flavonoid and element content of basil was examined, and then the dry matter, total polyphenol (TPC), flavonoid, crude fat, cruse protein and macroelement contents of the breads were determined. In the case of the breads, the measured values were reported on a dry matter basis.

3.2. Determination of total polyphenol content (TPC)

For the analysis, the method of Singleton et al. [17] was used for both basil and the breads. The samples were soaked in methanol (Scharlab S. L., Spain): distilled water (80:20), then the mixture was filtered through 292 pleated filter-paper (Sartorius Stedim Biotech S.A., Gottingen, Germany). 1 ml of the resulting solution was pipetted into a test tube, to which 2.5 ml of Folin-Ciocalteu reagent (VWR International S.A.S., France) was added. After 5 minutes, 2 ml of 75 g/l sodium carbonate (Scharlab S. L., Spain) was added to give a colored compound, the absorbance of which was measured with a spectrophotometer (Evolution 300 LC, Thermo Electron Corporation, England) at 760 nm. For the determination of the total polyphenol content, calibration solutions were prepared from a gallic acid (Alfa Aesar GmbH&Co. KG, Karlsruhe, Germany) stock solution. The absorbance of the calibration solution series was also measured, from the results of which a calibration curve was constructed. The total polyphenol content of our sample solution was determined using this curve. Results are given in mg GAE/100 g.

3.3. Determination of flavonoid content

Flavonoid content analytical results for both the spice and the breads are expressed in mg catechin equivalent per 100 g (mg CE/100 g). As a result of the added reagents, the solutions turned pink. Absorbances were measured at 510 nm with a spectrophotometer (Evolution 300 LC, Thermo Electron Corporation, England). The following reagents were used for the analysis: catechin (Cayman Chemical Company, USA), aluminum chloride (Scharlab S.L., Spain), sodium nitrite (Scharlau Chemie S.A., Spain), sodium hydroxide (Sigma-Aldrich Chemie Gmbh, Germany) and methanol (Scharlab S.L., Spain) [18].

3.4. Determination of element content

Sample preparation was carried out by the method of Kovács et al. [19] prior to measurement with ICP-OES (Inductively Coupled Plasma Optical Emission Spectrometer; Thermo Scientific iCAP 6300, Cambridge, UK). Samples were placed in digestion tubes and 10 ml of nitric acid (69% v/v, VWR International Ltd., Radnor, USA) was added to the tubes. The mixtures were allowed to stand overnight, and the next day pre-digestion was performed at 60 °C for 30 minutes. After cooling, 3 ml of hydrogen peroxide (30% v/v, VWR International Ltd., Radnor, USA) was added to the samples and the main digestion was performed at 120 ⁰C for 90 minutes. After this, the cooled samples were diluted with Milli-Q distilled water (Millipore S.A.S., Molsheim, France) and filtered through 388 filter paper (Sartorius Stedim Biotech S.A., Gottingen, Germany). The following element contents were determined by ICP-OES in the resulting digestates:

  • Ca 315.8 nm,
  • K 769.8 nm,
  • Mg 280.2 nm,
  • Na 818.3 nm,
  • P 185.9 nm,
  • S 180.7 nm.

Measurement wavelengths are indicated after the chemical symbols of the elements.

3.5. Determination of dry matter, crude fat and crude protein contents

Breads were tested for these parameters according to standard MSZ 20501-1:2007 [22].

3.6. Statistics

The experiments were performed in triplicate. SPSS statistical software (version 13; SPSS Inc. Chicago, Illinois, USA) was used to evaluate the results. This was also used to determine the mean and standard deviation. To determine statistically significant differences between the results, one-way analysis of variance (Tukey and Dunnett’s T3 test; P<0.05) was used.

4. Results and evaluation

4.1. Results of the examination of basil

Average values of basil analyses are shown in Table 2. This table shows the total polyphenol, flavonoid and element contents of the spice as determined by three replicate measurements.

Table 2. Basil measurement results

The polyphenol content values of basil were higher than the amounts (7.15-107 mg GAE/100 g) reported in the study of Moghaddam and Mehdizadeh [20]. However, in the dissertation of Kwee and Niemeyer [21], in which a study of 15 basil varieties was reported, the total polyphenol content ranged from 347 to 1,758 mg GAE/100 g. Based on our results, it can be stated that the value measured by us was high. In the case of basil, outstanding flavonoid content values are to be expected.

Based on our measurements, the spice has primarily high calcium and potassium contents, the values of which were over 20,000 mg/kg. A similar potassium content was measured by Özcan [4] in the case of dried basil (24,811 mg/kg), but the calcium content was much lower than the concentrations determined by us (12,363 mg/kg). The magnesium content of the plant is not negligible either, as a value of almost 8,000 mg/kg was measured from the alkaline earth metal. This is much higher than the values obtained by Özcan [4] and Özcan and Akbulut [3] (5,738 mg/kg and 3,130 mg/kg, respectively).

The analytical results of phosphorus and sulfur were also in the order of thousands in the given sample. Higher P (4,960 mg/kg) and lower S (1,923 mg/kg) contents were measured by Özcan [4] in dried basil of Turkish origin. Of the macroelements, sodium was found to have the lowest value, compared to the values reported by Özcan and Akbulut [3] (2,895 mg/kg).

4.2. Results of bread analyses

The results of bread nutritional value measurements are shown in Table 3.

Table 3. Bread nutritional value results on a dry matter basis

4.2.1. Results of dry matter content measurements

Dry matter contents of the bread samples were found to be similar. The dry matter content values of the samples ranged from 68.3% to 70.5%. Sample 4th had the lowest value, while the highest value was measured for sample 6th. Similar results were obtained for samples 1st, 2nd and 7th. For these values, no statistically verifiable differences were found. Compared to the other samples, however, the results of the before mentioned breads were significantly different. The sample with the highest dry matter content, which was sample 6th, differed significantly from all other samples. Nearly identical dry matter content values were measured for breads 4th and 5th, as well as for breads 3rd and 5th.

4.2.2. Results of total polyphenol content measurements

In terms of the total polyphenol content, it was found that even the control bread contained a certain amount of antioxidant compound, which was naturally the lowest of all samples. Kántor et al. [16] also found antioxidant compounds in their control bread. As the spices were added to the breads, the quantity of antioxidants increased steadily. The highest amount was measured in the case of sample 7th. The analytical results of all samples showed significant differences when compared to each other.

4.2.3. Results of total flavonoid content measurements

The quantity of flavonoids increased in proportion to the amount of spice added to the bread dough, similar to the polyphenol content. The lowest value was measured in the control sample, from which the flavonoid content of sample 2nd did not differ statistically, but in all other cases a significant difference was observed. Sample 7th, with a spice content of 25.5 g, had the highest flavonoid content.

4.2.4. Results of crude fat content measurements

In terms of crude fat content, the results were mixed. Values between 5.10% and 6.33% were measured. The highest fat content was determined in the control sample, while the lowest values were found in samples 4th, 5th and 6th. For breads 2nd and 3rd, the difference was only 0.1%. The fat content of bread sample 7th was higher than the previous results, but not more than that of the control sample. None of these results differed from each other in a statistically verifiable way, so it was found that there were no statistically verifiable differences in the fat contents of the breads made by us.

4.2.5. Results of protein content measurements

In terms of protein content, values that differed from each other were measured. The highest value was measured in the case of sample 6th, while the lowest was obtained in the case of the control sample. With the addition of basil, a minimal increase in the protein content was observed. There were no statistically verifiable differences in the case of the first 3 samples, however, the result of our control sample differed from the values of samples 4th, 5th, 6th and 7th. Samples with the highest protein content (5th and 6th) showed significant differences from samples 1st, 2nd, 3rd and 4th. The protein content of the bread enriched with the highest amount of spice differed only from the values obtained for samples 1st and 3rd, as in this case the protein content of the product was lower.

4.2.6. Results of macro element content measurements

Results of the macro element content measurements of the breads are summarized in Table 4.

Table 4. Bread macroelement content results on a dry matter basis

In the case of the control bread (sample 1st), our macro element content results were similar to those reported by Kántor et al. [16], with the exception of sodium (Ca: 510 mg/kg; K: 2,418 mg/kg; Mg: 285 mg/kg; Na: 3,180 mg/kg; P: 1,512 mg/kg; S: 948 mg/kg).

Regarding the amount of macroelements that could be measured in the samples, it was found that their amount increased in each case with increasing spice concentration. Sodium and sulfur are exceptions to this, as although slightly different results were obtained, this difference could be verified statistically. The results of basil show that these two elements have the lowest amounts in the plant. While the amounts of calcium, potassium, magnesium, phosphorus and sulfur in the bread showed lower values than in the spice itself, the sodium content increased significantly due to the addition of the same amount of table salt to the samples.

The calcium content of the samples ranged from 476 to 1,614 mg/kg. With the addition of basil, the calcium content increased gradually, in most cases by 200 mg/kg between the individual concentrations. Significant difference could not be detected only between samples 4th and 5th.

When determining the potassium content of the bread, values higher than those obtained for calcium were measured. Compared to the control sample, which contained 2,200 mg/kg of potassium, even the product with the lowest amount of spice showed a significant difference. The highest element content was measured in the case of sample 7th, which contained more than 1,200 mg/kg more potassium than sample 1st. There were no statistically verifiable differences between samples 4th and 5th, or 5th and 6th, however, there were significant differences in all other cases.

The magnesium content also increased, as shown by the results. Once again, the control sample had the lowest value, while sample 7th had the highest value. In terms of macro element content, the lowest values were measured for this element, as even the bread with the highest basil content did not reach a value of 1,000 mg/kg. Based on our results, it can be stated that there were statistically verifiable differences between the measured values in all cases.

The phosphorus content in the samples analyzed was between 1,478 and 1,623 mg/kg, these results being measured in samples 1st and 6th. As the amount of spice increased, the phosphorus content also increased. There were statistically verifiable differences between samples 1st-5th, 1st-6th, 1st-7th, 2nd-5th, 2nd-6th, 2nd-7th and 3rd-6th. In the other cases there were no differences in the phosphorus content.

5. Conclusion

At the beginning of the experiment, basil itself was examined, and its antioxidant compounds and macro element content were determined. As the results show, the spice itself has a very high total polyphenol and flavonoid content. In addition to these parameters, its macro element content is also significant, as it has outstanding calcium and potassium contents, which is also supported by the studies mentioned in the literature. Magnesium, phosphorus and sulfur were also measured in non-negligible amounts.

During the preparation of the breads, all the ingredients were added in the same amount, except for the spice, so it was expected that there would be differences as the amount of basil increased.

We cannot draw a clear conclusion as to why the dry matter content has changed this way. The expected result was that as the amount of spice increases, the dry matter content of the bread increases as well. Differences may have been due to the nature of the convection oven.

Both the total polyphenol content and flavonoid content results were in line with our expectations, as the addition of basil, containing a large amount of antioxidant compounds, to the bread significantly increased the values of these parameters, despite the fact that most of these compounds are heat sensitive.

No difference was found in the cruse fat content of the samples analyzed, so enrichment does not affect this parameter.

However, differences were observed in the protein content, as an increase in protein content was achieved by the enrichment. Further research is needed to answer the question as to why this value has increased.

In the case of the macro element content, with the exception of sodium and sulfur, a significant increase was achieved, which may be due to the high element content of basil.

Based on our studies and results, it can be said that enrichment with basil had a positive effect on most of the measured parameters. Higher intakes of antioxidant compounds and macronutrients are also beneficial, because these compounds are required for the normal functioning of the human body.

6. References

[1] Ghanjaoui M. E., Cervera M. L., Rhazi M. E., M. de la Guardia (2011): Validated fast procedure for trace element determination in basil powder. Food Chem. 125 (4) pp. 309-1313. DOI

[2] Pushpangadan P., George V. (2012): Basil. pp. 55-72 In: Peter K.V. (ed) Handbook of herbs and spices, Second Edition. Volume 1. Woodhead Publishing, Cambridge DOI

[3] Özcan M. M., Akbulut M. (2007): Estimation of minerals, nitrate and nitrite contents of medicinal and aromatic plants used as spices, condiments and herbal tea. Food Chem. 106 (2) pp. 852-858. DOI

[4] Özcan M. (2004): Mineral contents of some plants used as condiments in Turkey. Food Chem. 84 (3) pp.437-440. DOI

[5] Gisslen W. (2016): Professional Baking. Seventh Edition. John Wiley & Sons Inc. USA. p.792.

[6] Cauvain S. P. (2015): Technology of Breadmaking. Third Edition. Springer International Publishing. Switzerland. DOI

[7] Raba D. N., Moigrădean D., Poiană M-A., Popa M., Jianu I. (2007): Antioxidant capacity and polyphenols content for garlic and basil flavoured bread. J. Agroaliment. Proc. Technol. 13 (1) pp.163-168.

[8] Suleria H. A. R., Khalid N., Sultan S., Raza A., Muhammad A., Abbas M. (2015): Functional and Nutraceutical Bread prepared by using Aqueous Garlic Extract. Int. J. Food Safety. 17 pp. 10-20.

[9] Danza A., Mastromatteo M., Cozzolino F., Lecce L., Lampignano V., Laverse J., Nobile M. A. D. (2014): Processing and characterization of durum wheat bread enriched with antioxidant from yellow pepper flour. LWT Food Sci. Technol. 59 pp. 479-485. DOI

[10] Balestra F., Cocci E., Pinnavaia G. G., Romani S. (2011): Evaluation of antioxidant, rheological and sensorial properties of wheat flour dough and bread containing ginger powder. LWT Food Sci. Technol. 44 (3) pp. 700-705. DOI

[11] Lim H. S., Park S. H., Ghafoor K., Hwang S. Y., Park J. (2011): Quality and antioxidant properties of bread containing turmeric (Curcuma longa L.) cultivated in South Korea. Food Chem. 124 (4) pp. 577-1582. DOI

[12] Prokov T., Chonova V.., Slavov A, Dessev T., Dimitrov N., Petkova N. (2018): Effects on the quality and health-enhancing properties of industrial onion waste powder on bread. J Food Sci Technol. 55 (12) pp. 5091-5097. DOI

[13] Arufe S., Della Valle G., Chiron H., Chenlo F., Sineiro J., Moreira R. (2018) Effect of brown seaweed powder on physical and textural properties of wheat bread. Eur Food Res Technol 244 pp. 1-10. DOI

[14] Bourekoua H., Różyło R., Gawlik‑Dziki U., Benatallah L., Zidoune M. N., Dziki D. (2018): Evaluation of physical, sensorial, and antioxidant properties of gluten‑free bread enriched with Moringa Oleifera leaf powder. Eur Food Res Technol. 244 pp. 189-195. DOI

[15] Kowalczewski P. L., Walkowiak K., Masewicz Ł., Duda A., Poliszko N., Rożańska M. B., Jeżowski P., Tomkowiak A., Mildner‑Szkudlarz S., Baranowska H. M. (2019): Wheat bread enriched with raspberry and strawberry oilcakes: effects on proximate composition, texture and water properties. Eur Food Res Technol. 245 pp. 2591-2600. DOI

[16] Kántor A., Fischinger L. Á., Alexa L., Papp-Topa E., Kovács B., Czipa N. (2019): Funkcionális kenyér, avagy a fokhagyma és készítményei hatása a kenyér egyes paramétereire/Functional bread, or the effects of garlic and its products on certain parameters of bread. Élelmiszervizsgálati közlemények. 65 (4) pp. 2704-2714.

[17] Singleton V. L., Orthofer R., Lamuela-Raventos M. (1999): Analysis of total phenols and other oxidation substrates and antioxidants by means of folin-ciocalteu reagent. In: Abelson J, Simon M (ed) Methods in enzymology. Academic Press, California, pp. 152-178. DOI

[18] Kim D. O., Jeong S. W., Lee C. Y. (2003): Antioxidant capacity of phenolic phytochemicals from various cultivars of plums. Food Chem. 81 (3) pp. 321-326. DOI

[19] Kovács B., Győri Z., Csapó J., Loch J., Dániel P. (1996): A study of plant sample preparation and inductively coupled plasma emission spectrometry parameters. Commun. Soil Sci. Plant Anal. 27 (5-8) pp. 1177-1198. DOI

[20] Moghaddam M., Mehdizadeh L. (2015) Variability of total phenolic, flavonoid and rosmarinic acid content among Iranian basil accessions. LWT Food Sci. Technol. 63 (1) pp. 535-540. DOI

[21] Kwee E. M., Niemeyer D. E. (2011) Variations in phenolic composition and antioxidant properties among 15 basil (Ocimum basilicum L.) cultivars. Food Chem. 128 (4) pp. 1044-1050. DOI

[22] Magyar Szabványügyi Testület (MSzT) (2007): Sütőipari termékek vizsgálati módszerei. Magyar Szabvány MSz 20501-1. Magyar Szabványügyi Testület, Budapest.

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Use of unconventional plant raw material in poultry meat recipe

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Use of unconventional plant raw material in poultry meat recipe

DOI: https://doi.org/10.52091/EVIK-2021/3-4-ENG

Received: 2020. November – Accepted: 2021. March

Authors

1 South Ural State University (national research university), Chelyabinsk, Russian Federation

Keywords

semi-finished products from meat of broiler chickens, freeze-dried ground apples, Brazil nuts

1. Summary

The results of studying the combined use of freeze-dried ground apples (in an amount of 7%) and Brazil nut kernels (in an amount of 5 %) in the technology of baked poultry products are presented. The modification of the recipe made it possible to obtain stuffed meat products with improved consumer properties (apple and nut notes in the smell, slight sourish-sweetish tone in the taste, caramel shades in the color) and increased nutritional value (content of dietary fiber, mineral elements Mo, Au, Cu, B, Mn, W, Be, Sn, Fe, Ca, Mg, P, organic acids, protein) alongside a decrease in the amount of butter by 4%.

2. Introduction

Poultry meat is a dietary product with a high content of easily digestible proteins, low content of fat and cholesterol, it costs less than other meat, takes little time to cook and suits well for daily consumption [1]. However, today consumers tend to prefer “healthy” products, which makes producers expand the range of foods enriched with nutrients. This explains the relevance of using plant-based natural additives in meat processing industry, because they improve the quality characteristics of raw meat, and also increase nutritional and biological value of finished products [2].

It is a known fact that apple powder is rich in vitamins, organic and phenol carboxylic acids, monosaccharides, pectins, and dietary fiber, while the Brazil nut is considered a great source of complete protein, such mineral nutrients as Se, Cu, Mn, I, and fatty acids [3, 4, 5, 6, 7]. That is why, these plant raw materials are separately used in cakes, bread, chocolate, cutlets, curd cheese, cereal bars, nut and seed butters [8, 9, 10, 11, 12, 13, 14] to increase their nutrient density. The aim of our research was to study the possibility of combined use of freeze-dried ground apples and Brazil nut kernels in the technology of stuffed meat products with increased nutritional value.

3. Materials and methods

The following was used as materials of the research:

  • Chilled broiler chicken legs manufactured by OAO Turbaslinskiye Broilery (Republic of Bashkortostan, Blagoveshchensk) in accordance with GOST 31962-13;
  • Freeze-dried ground apples manufactured by PAO Sibirskiy Gostinets (Pskov Region, Moglino) in accordance with TU 10.39.25-001-34457722-18;
  • Kernels of Brazil nuts of Bolivian origin manufactured by OOO Komservis (Moscow Region, Mytishchi) in accordance with TU 9760-002-76440635-16;
  • Letniy Sad food additive manufactured by OOO Kulmbakh-D (Moscow Region, Krasnoarmeysk) in accordance with TU 10.89.19-008-58251238-20. Ingredients: dill, garlic, mustard, table salt, maltodextrin, dextrose, E621, dill extract, caraway extract, E100;
  • Chicken pockets with butter and herbs cooked according to TU 9214-013-64474310-12 by way of baking stuffed broiler chicken legs at 200 ˚C for 20 minutes.

Control samples were cooked according to a traditional recipe (Table 1), test samples were cooked adding 7% dried ground apples, 5% crushed Brazil nut kernels and 4% less butter.

Table 1. Recipe for Laboratory Samples of Chicken Pockets

The dosages of the plant raw materials were chosen taking into account the known data published in a number of scientific papers [8, 9, 10, 11, 12, 13, 14] The test samples of chicken pockets were cooked using deboned chicken legs with skin, flat in shape, with a longitudinal cut in the form of a pocket filled with butter, mixed herbs, ground dried apples, and Brazil nut kernels. The cut was joined with skewers.

The plant raw materials were tested for the content of protein and fat according to MU 4237-86, sugar – GOST 8756.13-87, table salt – GOST 15113.7-77, starch – using standard approach [15]. The meat and meat products were tested for protein according to GOST 25011-2017, fat – GOST 23042-2015, moisture – GOST 9793-2016, table salt – GOST 9957-2015. Sensory evaluation of the laboratory samples was carried out according to GOST 9959-2015. The content of dietary fiber in all samples was determined using the traditional approach [15], content of organic acids – according to М 04-47-12, mineral elements – using iCAP 7200 DUO emission spectrometer.

All measurements were carried out in three replications. Statistical analysis was performed using Microsoft Excel XP and Statistica 8.0 software package. The statistical error of the data did not exceed 5% (at 95% confidence level).

4. Results and discussions

Analyzing the nutritional composition of the non-traditional plant raw materials in comparison with poultry meat (Table 2), it was found that Brazil nut kernels contained a relatively high amount of lipids (11 times more), which made it possible to reduce the amount of butter in the recipe, and hence to decrease cholesterol content in the test samples.

Table 2. Nutrient Composition of Materials under Study

Apple powder proved to have relatively high levels of sugars, dietary fiber, and organic acids, in comparison with both raw meat and other plant components. It is well known that non-volatile acids in fruits not only determine taste and aroma of finished products, but also contribute to the production of gastric juice and have a choleretic effect [16], while insoluble (lignin, cellulose, chitin) and soluble (pectin, inulin) dietary fiber is able to effectively bind heavy metal ions and organic substances [17]. All these factors a priori suggest that this new component in the chicken pockets recipe should have a positive effect on the human organism.

The amino acid content in Letniy Sad food additive was due to sodium glutamate (E621) in its composition, while the presence of table salt at the level of 34.9 ± 2.2% allowed not to introduce any more of it.

The mineral composition of all plant components turned out to be richer than that of broiler chicken legs in terms of the number of elements (Table 3). In terms of the content of micronutrients, which have great physiological importance for the human organism, the Brazil nut contained 12 times more Ca, 7.4 times more Fe, 7.2 times more Se, 6.3 times more Mg, 3.6 times more P and Zn, but the Cu, Mn and Co content were also higher than in the poultry meat. Similarly, the dried ground apple powder contained 2.4 time more Fe, 2 times more Ca and 2.7 times more Si, additionally it’s Ag, Au, B, Be, Cu, Ga, Mn, Mo contain were also higher, than the content of poultry meat. Considering 0.5% dosage of Letniy Sad food additive as per the recipe, its contribution to the total mineral value of ready chicken pockets can be considered significant only in terms of Na content, which was 38 times more than in raw meat.

The levels of heavy metals in nuts – As, Cd, Pb, not found in semi-finished meat products, did not exceed the regulated norms of TR CU 021/2011.

Chilled chicken legs had a relatively high content of K, Si, as well as Na.

Table 3. Mineral Composition of Materials Under Study

Thus, it was proved efficient to use such plant components in the technology of baked meat products in order to increase their nutritional value.

Tasting of the laboratory samples of chicken pockets established that apple and nut raw materials in the specified ratio had a positive effect on the consumer characteristics of the product. At the same time, the control sample did not have outstanding taste and aromatic properties, with creamy tones predominant, leveling the characteristics of a meat product. The mixture of the plant materials accounted for the formation of apple and nut notes in the smell and a slight sour-sweet tone in the taste of the products. The color on the cut acquired a caramel shade. The appearance, consistency, and juiciness of all samples were consistently high.

When testing physical and chemical indicators, it was found that the samples under study did not differ significantly in moisture, fat, and sodium chloride content (Table 4). However, the test samples contained slightly more protein (by 2.1 %), as well as dietary fiber and organic acids, which is a benefit from the standpoint of modern nutritional science.

Table 4. Nutrient Composition of Laboratory Samples of Chicken Pockets

The study of the mineral composition of the laboratory samples revealed that the test samples exceeded the control ones in terms of the amount of most macro- and microelements (Figures 1, 2). Specifically, as for macronutrients, baked samples with a modified recipe contained more Ca (1.7 times), Mg (35.4 %), and P (20 %); as for microelements – more Mo (473 times), Au (132 times), Cu (56 times), B and Mn (28 times), W (20 times), Be (17 times), Sn (15.8 times), Fe and Ti (1.5-1.6 times), Se (1.4 times), Zn (23.1 %), etc.

Figure 1. Macroelement Composition of Laboratory Samples of Chicken Pockets
Figure 2. Microelement Composition of Laboratory Samples of Chicken Pockets

Furthermore, the amounts of microelements established according to MR 2.3.1.2432-08 satisfy the daily demand of an adult in Mo by 30.4 %, Cu - by 4.3%, Mn - by 2.1 % if one eats 100 g of baked poultry meat products with the added apple powder and Brazil nut.

Minerals are essential for the human body. They are a part of tissues, hormones, enzymes, intracellular fluid. They are needed for the formation of blood and bone cells, functioning of the nervous system, regulation of muscle tone, processes of energy generation, growth and recovery of the body [18, 19].

5. Conclusions

The nutrient composition of the raw materials and finished products was studied. We found that it is possible to use freeze-dried ground apples (in an amount of 7%) and Brazil nut kernels (in an amount of 5 %) together in the recipe of stuffed meat products. Modifying the recipe for chicken pockets, we obtained a product with improved consumer properties, increased nutrition value, and a decrease in the amount of butter by 4%.

6. Acknowledgement

The work was supported by Act 211 of the Government of the Russian Federation, contract № 02.A03.21.0011.

7. References

[1] Denisyuk, E. A., Tyurina, E. O. (2019): Effect of spinach on food value and economic efficiency of poultry meat semi-finished products production in conditions of LLC “Pervy Myasokombinat”. Bulletin of the Nizhny Novgorod State Agricultural Academy, 4 (24), pp. 28-32.

[2] Asfondyarova, I. V., Sagaidakovskaia, E. S. (2018): Meat semi-finished products of high nutritional and biological value. XXI Century: Resumes of the Past and Challenges of the Present, 7(43), pp. 87-92.

[3] Kishtikov, Kh. B., Dzhappueva, Zh.R. (2017): Chemical composition and curative, dietary, and preventative functions of fruit and vegetable powders added to bakery goods made of wheat flour. Alley of Science, 4(9), pp. 789-796.

[4] Pyanikova, E. A., Cheremushkina, I.V., Kovaleva, E.A., et al. (2020): The effect of apple powder on the consumption of crispbread. Bulletin of Voronezh State University of Engineering Technology. 82(1), pp. 157-163. doi.org/10.20914/2310-1202-2020-1-157-163

[5] Kantoroeva, A. K. (2019): Analysis of the development of the world market for nut crops. Economics and Management: Problems, Solutions. 2(3), pp. 147-154.

[6] Klimova, E. V. (2008): Comparative study of total oil content, fatty acid profile, peroxide value, concentration of tocopherol, phytosterol and squalene in the kernels of Brazil nuts, pecans, pine nuts, pistachios and cashews. Food and processing industry. Abstract journal. 2, p. 369.

[7] Martins, M., Klusczcovski, A.M., Scussel, V.M. (2014): In vitro activity of the brazil nut (bertholletia excelsa h. b. k.) oil in aflatoxigenic strains of aspergillus parasiticus. European food research and technology. 239(4), pp. 687-693.

[8] Nurgalieva, A. A., Pusenkova, L. I. (2017): Use of apple powder in baked confectionery products. Alley of Science. 3(10), pp. 241-248.

[9] Perfilova, O. V. (2019): Development of a new method for preparing white flour dough using apple and pumpkin powder. New Technologies. 1(47), pp. 141-148. doi.org/10.24411/2072-0920-2019-10114.

[10] Linovskaya, N. V. (2019): Development of chocolate with finely ground additions. Scientific works of the Kuban State Technological University” electronic network polythematic journal. 9, pp. 114-123.

[11] Mogilniy, M. P. (2017): Evaluation of the biological value of minced meat products with fruit fillings. Modern Humanities Success). 2(6), pp. 57-62.

[12] Ukkonen, T. I., Belozerova, M. S. (2017): Development of curd cheese with increased selenium content. Materials of the VIII International Scientific and Technical Conference «Low-temperature and food technologies in the XXI century». pp. 264-267.

[13] Patent No. 2706159 RF. Cereal bar for nutrition of those working with harmful compounds of arsenic and phosphorus. Kazan National Research University. Gumerov T. Yu., Gabdukaeva L. Z., Shvink K. Yu. Application dd. 14.05.2019; published 14.11.2019.

[14] Patent No. 2603892 RF. Method for preparing nut-like mass. Rodionova N. S., Popov E. S., Alekseeva T. V., Sokolova O. A., Shakhov A. S. Application dd. 01.07.2015; published 10.12.2016.

[15] Skurikhin, I.M., Tutelyan, V.A. (1998): A guide to the methods of analyzing food quality and safety. Moscow, Brandes, Medicine, p. 342.

[16] Nechaev, A. P., Traubenberg, S. E., Kochetkova, A. A., et al. (2012): Food Chemistry: 5th edition, revised and expanded. – SPb.: Giord, p. 670.

[17] Nikiforova, T. E., Kozlov, V. A., Modina, E. A. (2010): Solvation-coordination mechanism of sorption of heavy metal ions by cellulose-containing sorbent from aqueous media. Chemistry of plant raw material. 4, pp. 23-30.

[18] Dydykina, I. S., Dydykina, P. S., Alekseyeva, O. G. (2013): Trace elements (copper, manganese, zinc, boron) and healthy bone: prevention and treatment of osteopenia and osteoporosis. Effective Pharmacotherapy. 38, pp. 42-49.

[19] Krutenko, V. V. (2013): A close look at the role of gold trace element in the human body. Bulletin of problems of biology and medicine. 2(3), pp. 19-24.

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Near-infrared spectroscopy: rapid and effective tool for measuring fructose content

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Near-infrared spectroscopy: rapid and effective tool for measuring fructose content

DOI: https://doi.org/10.52091/JFI-2021/1-1-ENG

Received: October 2020 – Accepted: January 2021

Authors

1 Department of Nutritional Science and Production Technology, Faculty of Agricultural and Environmental Sciences, Szent István University, Kaposvár Campus
2 Department of Physics and Control, Faculty of Food Science, Szent István University, Budapest Campus
3 Adexgo Kft., Balatonfüred, Hungary
* Corresponding author: bazar@agrilab.hu

Keywords

Fructose (fruit sugar), sugars, °Brix, NIR-spectroscopy (near-infrared), adverse physiological effects of fructose, metabolic disorder, cardio-vascular diseases, pre-treatment of spectra, valence vibration, harmonic vibration, statistical spectra analysis

1. summary

Since high fructose intake was found to be associated with increased health risks, it is important to raise awareness towards the amount of this widely used sugar within foods and beverages. The rapid and accurate detection and quantification of sugar types is not an easy task using conventional laboratory technologies. Near-infrared (NIR) spectroscopy has been proven to be a useful tool in this regard, and the present study highlights the applicability of this rapid correlative analytical technology in the measurement of fructose concentration against that of other sugars in aqueous solutions of sweeteners. The presented NIR calibrations are accurate for the relative measure of °Brix (R2 = 0.84), and the direct measurement of the individual sugars (R2 > 0.90) even in solutions with multiple sugars.

2. Introduction

Food sweeteners have become the most widely used additives in the food processing industry, especially in the production of beverages and other products such as desserts and yoghurts. One of the oldest sweetener to have been documented in history is honey [1]. This, and some of the traditional sweeteners such as maple syrup, carob, and agave, consumed for decades are largely made up glucose, fructose, sucrose, minerals and other compounds [1]. Glucose is almost always present in foods and plays an essential role in the regulation of metabolism in human. It can be ingested either as free available sugar (glucose powder) or bonded in polymers, in the case of starch, dextrin, and maltodextrins. Glucose could also be bonded in disaccharides, like in the case fructose bond to glucose in sucrose [2].

For some time now, concerns about the form and levels of sweeteners used in the food industry, and the °Brix value of processed foods, have been topical due to the health implications of the consumers. This is mainly because of the risk of developing metabolic abnormality (diabetes) associated with high intake of sugar, especially sugar of high fructose content. Consumers are therefore becoming more conscious of what they consume, and will at times prefer a reduction of the caloric levels of processed foods, consequently reducing sugar intake [3].

High fructose intake was found to be associated with a high risk of metabolic syndrome [4], obesity, diabetes and an increase in blood triglyceride concentrations and insulin resistance compared with high glucose intake [5], [6], [7]. High risk of cardiovascular diseases and even malignant tumors in body tissues may be related to excessive fructose intake [8], and also dyslipidemia and kidney diseases [9].

Over the years, the application of near-infrared spectroscopy (NIR) to analyze the forms of sugar in food sweeteners, has been found to be easier, faster and cost-efficient [10] compared with tedious and reagent involving methods, such as gas chromatography (GC), high-performance liquid chromatography (HPLC) and enzymatic analysis [11], [12]. The HPLC is the most frequently used method for assessing free fructose, free glucose, sucrose, maltose, and lactose content [13].

The NIR spectral region is found between 800 to 2500 nm (12500–4000 cm−1) range, with absorptions representing overtones and combinations which are associated with –CH, –OH, –NH, and –SH functional groups [14]. In the case of glucose, 1st overtone of O–H stretching corresponds to absorption bands at 1195, 1385, 1520, 1590, 1730 nm, 1st overtone of O–H stretching of fructose and sucrose at 1433 nm, and O–H combination band of sucrose, glucose and fructose at 1928 nm [14].

Mono- and disaccharides, such as glucose, fructose, sucrose, lactose, were also analyzed in aqueous solutions [15]. Although the same molar concentrations of all the concerned sugars were dissolved, the mass that those represented differed considerably due to the differences in molecular weights of mono- and disaccharides. When quantifying sugars in mixtures, the molar concentration of the sugar solutions gave less accurate calibration models compared with those fitted on weight per volume concentration. Since the spectral information is mostly the light absorbance of chemical bonds during excitation, this information is more proportional with the number of chemical bonds and atoms in the aqueous solution, than with the number of molecules. Regression coefficient vectors of calibration models for each of the sugars also revealed the spectral regions holding the highest importance in the quantitative analysis of the sugars. Regression vectors of the 1100-1800 nm interval, associated with signals of O–H and C–H bonds, showed the significance of characteristic spectral regions of water and the dissolved sugars. The calibration on the concentration of the sugars within the mixtures showed accurate validation performance even at low concentration levels (0.0018 – 0.5243 g/cm3), R2CV of 0.841 and 0.961, SECV of 0.024 g/cm3 and 0.012 g/cm3 for glucose and fructose, respectively. This showed possible quantification of a specific sugar in a mixture of sugars in a solution using NIR spectroscopy [15].

In related studies [10], [16], [17], glucose, fructose and sucrose were quantified in different fruit juices using NIR, and accurate partial least square regression (PLSR) models were reported (R2 > 0.854, 0.963, 0.953 for glucose, fructose, sucrose, respectively). Good PLRS models were reported for predicting glucose within 900-2200 nm wavelength range [18], whereas the 900-1650 nm interval was reported to be good for the discrimination of organic sugar and conventional brown sugar using partial least squares discriminant analysis (PLS-DA) models [19]. In a study, the concentration of glucose in an aqueous mixture of glucose, albumin and phosphate was quantified using NIR and reported accurate PLRS models [20]. The possibility to predict the glucose, fructose and sucrose content in Morindae officinalis extracts utilizing NIR was also reported [21].

The Hungarian food industry is flooded with many sweeteners for food processing. However, there are three major sweeteners: K-syrup LDX and K-sweet F55, which are two commonly used isosugars, and D-sucrose. K-syrup LDX is a sweet, viscous, quickly crystallizing syrup often used in food and pharmaceutical industry as a raw material for fermentation. It contains high amount of glucose or dextrose (93%), and small amount of fructose (0.5%) and viscous liquid [22]. K-sweet F55, however, is a high caloric isosugar consisting of glucose and fructose, where the fructose content is higher (55%) than the glucose (45%) [23], and the third sweetener is D-sucrose or refined sugar, which is increasingly being replaced with K-syrup LDX and K-sweet F55.

This study aimed to determine the applicability of NIR spectroscopy to quantify glucose, fructose, sucrose content and °Brix of aqueous solutions of the widely used sweeteners, D-sucrose, K-syrup LDX, and K-sweet F55.

3. Materials and Methods

3.1. Sample preparation

Three kinds of sugars were used with brand names: D-sucrose (Carl Roth GmbH, Karlsruhe, Germany): 100% sucrose; K-Syrup LDX (KALL Ingredients Kft., Tiszapüspöki, Hungary): 93% glucose + 0.5% fructose + 6.5% water; K-Sweet F55 (KALL Ingredients Kft., Tiszapüspöki, Hungary): 45% glucose + 55% fructose. Aqueous solutions were prepared at 10 different concentrations for each of the three sugars, separately. A total sample of 30 samples was prepared, 100 ml of each.

3.2. Laboratory measurement

°Brix was measured with Hanna HI96801 Digital Refractometer, and recorded as reference for subsequent NIRS calibrations. Glucose, fructose and sucrose concentration of the respective sugar solutions was calculated based on the mass of sweetener added to the solutions and the percentages of the individual sugars within the sweeteners. The following formulas were used for the calculation of glucose and fructose in K-sweet F55 and K-syrup LDX solutions:

  1. Glucose in K-syrup LDX solution = 93/100*amount of K-syrup in solution (g/100g)
  2. Fructose in K-syrup LDX solution = 0.5/100*amount of K-syrup in solution (g/100g)
  3. Glucose in K-Sweet F55 solution = 45/100*amount of K-sweet F55 in solution (g/100g)
  4. Fructose in K-sweet F55 solution = 55/100* amount of K-sweet F55 in solution (g/100g)

Accordingly, each of the 30 samples was described with °Brix, and concentrations of total sugar, glucose, fructose and sucrose, as listed in Table 1.

Table 1. The °Brix, concentration of total sugar, glucose, fructose and sucrose of the aqueous sugar solutions used for the study

D-sucrose: 100% sucrose; K-syrup LDX: 93% glucose+0.5% fructose; K-sweet F55: 45% glucose+ 55% fructose; SD: standard deviation; Max: maximum value; Min: minimum value

3.3. NIRS measurement

The samples were scanned at room temperature (25 °C) using a FOSS NIRSystems 6500 (FOSS NIRSystems, Inc, Laurel, MD, USA) spectrometer, operated with WinISI v1.5 software (InfraSoft International, Port Matilda, PS, USA). The scanning was done in transmission mode after measuring 1 ml sugar solution into a quartz cuvette having 1 mm pathlength. Two rounds of scanning of each sample were done randomly, and the subsequent sample was used to wash the cuvette three times between each sample scanning. Sixty spectral data were obtained and the spectra of the two rounds were averaged resulting in 30 spectra.

3.4. Spectral pre-processing and multivariate data analysis

The Unscrambler v9.7 (CAMO Software AS, Oslo, Norway) software was used for the analysis of the NIR data, while the MS Excel 2013 was used to calculate the descriptive statistics for the variables measured and calibrated for °Brix, glucose, fructose, and sucrose concentration.

For scatter correction of spectra, and to obtain accurate and robust calibration models, several spectral types of preprocessing were performed: standard normal variate (SNV), multiplicative scatter correction (MSC) and gap-segment second derivative (2nd order derivative, gap of 5 data points, segment of 5 data points).

Using multivariate data analyses, both the separation of the solutions prepared with different sweeteners and the calibration on the targeted quantitative parameters was performed. Principal component analysis (PCA) [24] was used to investigate the multidimensional pattern of the spectra data and to identify differences among the three groups of the sweetener solutions. The spectral data within the NIR range (1100-1850nm) were calibrated with the laboratory data as the reference, using partial least squares regression (PLSR) models [24]. The optimum number of latent variables (LV) used for the PLSR modelling was determined by full (leave-one-out) cross-validation, when in a 30-step iterative process each of the 30 samples was left out of the calibration once and was used for validating the model [24].

Evaluation of PLSR models was done by comparing the calibration statistics with that of the cross-validation. The determination coefficient of calibration (R2C) and cross-validation (R2CV), and the root mean square error of calibration (RMSEC) and cross-validation (RMSECV) were compared, where larger R2 value and smaller RMSE value represent the better model. During the model optimization processes, RMSECV values were minimized.

4. Results and discussions

The recorded raw spectra show the typical NIR absorption of water, with a major peak at 1450 nm, representing the 1st overtone region of O–H bond (Figure 1). The small peak around 1780 nm represents the 1st overtone of C–H bonds. The second derivative spectra were calculated with the gap-segment derivative function, where both gap and segment were set to 5 data points to avoid noise enhancement of the derivative function, still keeping the useful signals within the pretreated data.

Figure 1. Raw spectra of the sugar solutions in the range of 1100-1850 nm

The negative peaks of the 2nd derivative spectra (Figure 2) indicate the locations and relative amplitude of the original overlapping absorptions appearing as one in the raw spectra. This shows the well-described phenomenon that major peak of the raw spectrum at 1450 nm is formed by at least two underlying absorptions of water at 1416 nm and 1460 nm, representing less and more H-bonded water, respectively [15].

The applied spectral pretreatments (2nd derivative, or SNV, or MSC) did not allow visual differentiation of solutions with different sweeteners, while the gradual changes of the water absorption peaks indicated the effect of the increasing concentration of dissolved sugars on the structure of water [15].

Figure 3 shows the 3D plot of the PCA performed with 2nd derivative spectra of all the 30 solutions. The solutions of the three types of sweeteners are indicated with different colors and numbers. The two plots show the same result from different angles, highlighting that 4th principal component (PC4) is responsible for the separation of K-Sweet F55 from D-sucrose and K-Syrup LDX, and PC2 is responsible for the separation of D-sucrose from K-Syrup LDX and K-Sweet F55. Thus, PC2, as new latent variable covering approximately 2% of the variance of the original NIR data, describes the difference between the disaccharide and monosaccharide solutions, while PC4, covering less than 1% of the variance of the original NIR data, describes the difference between the solutions of high fructose syrup and that of the other sweeteners. The combination of PC2 and PC4 describes the differences between glucose solutions and others.

Figure 2. Second derivative spectra of the sugar solutions in the range of 1100-1850 nm
Figure 3. 3D plots of the principal component analysis (PCA) of the three types of sugar solutions using 2nd derivative spectra, showing (a) the 1st principal component (PC1), PC2 and PC4, and (b) PC1, PC4 and PC2. The red (1), green (2) and light blue (3) scores represent D-sucrose, K-Syrup LDX, and K-Sweet F55, respectively.

Figure 4 shows the loading vectors of PC2 and PC4. The wavelength regions having the largest deviation from zero are the most responsible for score values of the principal components, thus, the assigned peaks indicate the absorptions causing the difference between the sugar solutions. The band assignments are in good harmony with previous findings [14,15], i.e. peaks in the 1300-1600 nm interval refer to the molecular changes of water caused by the dissolved sugars, while the peaks in the 1600-1850 nm interval represent characteristic C–H bands.

The results of the calibration models developed using PLS regression on the measured °Brix and calculated fructose, glucose, sucrose concentrations are presented in Table 2 and Figure 5.

Figure 4. The loading vectors of PC2 and PC4 showing the absorption bands responsible for the separation of D-sucrose from K-Syrup LDX and K-Sweet F55, and for the separation of K-Sweet F55 from D-sucrose and K-Syrup LDX, respectively
Table 2. The calibration and cross-validation statistics for °Brix, glucose, fructose and sucrose concentration in the sugar solutions (n = 30), highlighting the best model for each

LV: number of latent variables, R2C: determination coefficient of calibration, RMSEC: root mean square error of calibration, R2CV: determination coefficient of cross-validation, RMSECV: root mean square error of cross-validation, MSC: multiplicative scatter correction, SNV: standard normal variate, 2D5G5S: 2nd order derivative with 5-point gap and 5-point segment

The best results for °Brix were achieved with no spectral pretreatment. The RMSE of °Brix remained around 1 °Bx, which was almost third of the standard deviation of the measured reference values. The RMSE of the sugar concentrations was similarly low. The least accurate model was achieved for fructose, which is caused by the group of samples with fructose content below 0.05% - for these samples the model performed worse than in the higher concentration regions (Figure 5. (b)). Second derivative pretreatment gave the best result for glucose and sucrose, while the best models for fructose were achieved without pretreatment of the NIR spectra.

The calibration and cross-validation regression lines (Y-fit) of the best °Brix, fructose, glucose and sucrose models are shown in Figure 5. The black diagonal line shows the optimal Y-fit, while blue and red lines show the calibration and cross-validation Y-fits. The blue dots show the NIR predicted composition values of samples during calibration in the function of the laboratory reference values, and red dots show the NIR predicted values at cross-validation testing, again, in the function of the reference values measured. The closer the dots are to the regression line and the less the regression line deviates from the optimal Y-fit, the better the calibration model is. In most of the cases, the achieved Y-fits are hitting the optimum, meaning that the NIR predicted values are almost equal to the actual laboratory reference values. The calibration and cross-validation results of this study are in agreement with the previously cited results achieved with sugar solutions and fruit juices. These results confirm that, after a proper calibration process, NIR spectroscopy is a useful and effective tool for easy, rapid and accurate measurement of individual sugars in mixed solutions.

Figure 5. The optimum Y-fit (black diagonal) and the Y-fits of the best calibrations (blue) and cross-validations (red) for (a) °Brix, concentration of (b) fructose, (c) glucose and (d) sucrose

5. Conclusions

The results of this study performed with widely used sweeteners confirm the previously published findings that NIR spectroscopy is a useful and powerful technology to detect and quantify individual sugar types even in mixture solutions. Since NIR spectrometers have not only reached the portable size but have become extremely small as a fingernail-sized chip, the importance of this technology in everyday food qualification seems to be underestimated. Wide aspects of applications should be tested and used for monitoring products and warrant food safety and quality. Among these applications, checking and certifying the fructose content of beverages and foods would advantage consumers’ health, as this constituent has been proven to raise the risk of several diseases of modern times. NIR spectroscopy as secondary correlative analytical technology will likely remain to be unsuitable for detecting and quantifying fructose in a complex liquid of completely unknown composition, but may be suitable for indicating the excessive presence of fructose in a known liquid meant to be containing no or only a certain amount of fructose. The usability of NIR tools is limited and they should not be considered as subtituents of classical analytical methods, however, by rational use of opportunities, useful applications can be developed for practice.

6. References

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[12] Yuan, H., Wu, Y., Liu, W., Liu, Y., Gao, X., Lin, J., & Zhao, Y. (2015): Mass spectrometry-based method to investigate the natural selectivity of sucrose as the sugar transport form for plants. Carbohydrate Research, 407, pp. 5-9.
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[13] International Association of Analytical Chemistry, A. (1990): Official Methods of Analysis of the AOAC. Arlington VA

[14] López, M. G., García-González, A. S., & Franco-Robles, E. (2017): Carbohydrate Analysis by NIRSChemometrics. In K. G. Kyprianidis & S. Jan (Eds.), Developments in Near-Infrared Spectroscopy pp. 81-95
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[15] Bázár, G., Kovacs, Z., Tanaka, M., Furukawa, A., Nagai, A., Osawa, M., Itakura, Y., Sugiyama, H., Tsenkova, R. (2015): Water revealed as molecular mirror when measuring low concentrations of sugar with near infrared light. Analytica Chimica Acta, 896, pp. 52-62.
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[16] Xie, L., Ye, X., Liu, D., & Ying, Y. (2009): Quantification of glucose, fructose and sucrose in bayberry juice by NIR and PLS. Food Chemistry, 114 (3), pp. 1135-1140.
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[17] Rodriguez-Saona, L. E., Fry, F. S., McLaughlin, M. A., & Calvey, E. M. (2001): Rapid analysis of sugars in fruit juices by FT-NIR spectroscopy. Carbohydrate Research, 336 (1), pp. 63-74.
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[18] Mekonnen, B. K., Yang, W., Hsieh, T. H., Liaw, S. K., & Yang, F. L. (2020): Accurate prediction of glucose concentration and identification of major contributing features from hardly distinguishable near-infrared spectroscopy. Biomedical Signal Processing and Control, 59, pp. 101923.
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[19] De Oliveira, V. M. A. T., Baqueta, M. R., Março, P. H., & Valderrama, P. (2020): Authentication of organic sugars by NIR spectroscopy and partial least squares with discriminant analysis. Analytical Methods, 12, pp. 701-705.
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[21] Hao, Q., Zhou, J., Zhou, L., Kang, L., Nan, T., Yu, Y., & Guo, L. (2020): Prediction the contents of fructose, glucose, sucrose, fructo-oligosaccharides and iridoid glycosides in Morinda officinalis radix using near-infrared spectroscopy. Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, pp. 234
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[22] KALL, I. (2020): K-syrup LDX. Retrieved from http://kallingredients.hu/en/products/2/14/k-syrup-ldx

[23] KALL, I. (2020): K-sweet F55. Retrieved from http://kallingredients.hu/en/products/2/12/k-sweet

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Regulation of nutrition labeling of foods in the European Union and Hungary; A historical review from the beginning to the present day

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Regulation of nutrition labeling of foods in the European Union and Hungary; A historical review from the beginning to the present day

DOI: https://doi.org/10.52091/JFI/2021/1-2-ENG

Received: November 2020 – Accepted: January 2021

Authors

1 Ministry of Agriculture, Department of Food Economics and Quality Policy
2 Hungarian University of Agriculture and Life Sciences
3 National Institute of Pharmacy and Nutrition
4 University of Veterinary Medicine
5 National Food Chain Safety Office, Directorate for Risk Management

Keywords

Food labeling, nutrition labeling, voluntary labeling, mandatory labeling, Codex Alimentarius Commission, harmonization of food law, Big 8, Big 4, Traffic light, Battery, Nordic Keyhole, Nutri Score, GDA (Guideline Daily Amount)

1. Summary

Food labeling is one of the most diverse areas of food law, and special attention is paid to nutrition labeling within this area. This is not a coincidence, as modern nutrition science is evolving year by year, and legal changes must also keep pace with this. Nutrition labeling is particularly important for those who struggle with obesity or certain metabolic diseases or have special nutritional needs for other reasons. In a somewhat unusual way, the regulation of nutrition labeling has not appeared primarily in regulations at the national level, but its development began within an international framework, with the first breakthrough being the Codex Alimentarius and the expert work carried out within it. Hungary has been participating in this work since the beginning, so the Hungarian regulation, regardless of historical periods, has been relatively harmonized with the current best labeling practices in the world, with complete harmonization taking place by the time Hungary was on the verge of joining the European Union. In this study, we look back at the most important international, EU and Hungarian steps in the development of the regulation, not only presenting legal changes, but also comparing them to the changing requirements of the various periods. In addition to the current regulatory environment and challenges for nutrition labeling, key voluntary labeling schemes are also included in this communication.

2. Introduction

At the international level, the cornerstone of nutrition labeling decree was laid in 1985 by the Codex Alimentarius established by the FAO/WHO, in the form of a guide to nutrition labeling. Nutrition labeling decree was based both in the European Union and Hungary on the Codex Alimentarius (Figure 1).

Figure 1. Regulatory relationships of the nutrition labeling of foods

* MÉM-SZEM: former Ministry of Agriculture and Food – former Ministry of Social Affairs and Health

** MÉ: Codex Alimentarius Hungaricus

*** FM-NM-IKM: former Ministry of Agriculture – former Ministry of Public Health – former Ministry of Industry and Trade

Nutrition labeling was first regulated by the Council of the European Community in 1990 with the adoption of Directive 90/496/EEC. Compliance with the directive was voluntary and it applied to all foods intended for normal public consumption (Figure 2).

In Hungary, indicating the “essential” elements of nutrition labeling of foods was voluntary until the mid-1970s and 1980s, then, from 1988, indication of the energy content became mandatory. From 1996, rules for the nutrition labeling of foods had been defined by the Hungarian Food Codex (Codex Alimetarius Hungaricus), with a definite content but still on a voluntary basis [1], and this remained in force until December 13, 2014.

Prior to December 13, 2014, nutrition labeling was a mandatory element on the packaging only if the manufacturer, using today’s regulatory terminology, made a nutrition or health claim, or such a claim was published in relation to the product, or if it was a food for people with special nutritional needs (e.g., baby food) [2].

In the meantime, more and more countries have introduced mandatory nutrition labeling at the international level, mainly for public health purposes, in order to reduce obesity and to prevent certain chronic diseases [3]. Recognizing the growing public interest in the link between the diet and health [4] and also because solutions were needed to the health challenges related to overweightness and obesity [5,6], it has become clear that the creation of harmonized rules at the EU level was urgent and essential to ensure adequate consumer information. In light of this, Regulation (EU) No 1169/2011 on the provision of food information to consumers was adopted, which defines the general principles, requirements and obligations for the labeling of foods, and also makes it mandatory to indicate the nutrient content of foods. The primary purpose of nutrition labeling is to provide information to consumers about the nutritional composition of foods, helping them to make informed decisions [7].

Figure 2. Chronological summary of laws governing labeling of nutrition value

* MÉM: former Ministry of Agriculture and Food

** MÉM-SZEM: former Ministry of Agriculture and Food – former Ministry of Social Affairs and Health

*** FM-NM-IKM: former Ministry of Agriculture – former Ministry of Public Health – former Ministry of Industry and Trade

Of course, food labeling alone is not enough. In order for information to achieve its purpose, it is also necessary to motivate consumers and for them to know the principles of good nutrition. Education and informing consumers for educational purposes are indispensable for consumers to better understand food information and thus incorporate the given foods correctly into their own diets [8, 9, 10, 11, 12, 13, 14].

In this article, the development of the European Union and Hungarian regulations regarding the nutrition labeling of foods intended for normal public consumption are described, as well as the related practices and experiences. Due to the complexity of the topic, laws on foods for special dietary uses and on foods containing claims are not discussed in detail in this publication.

3. Nutrition labeling at the international level (Codex Alimentarius)

3.1. Operation and purpose of the Codex Alimentarius Commission

The main purpose of the Codex Alimentarius Commission (hereinafter referred to as the: Codex), operating within the framework of the specialized agencies of the United Nations FAO and WHO, is to develop food standards, guidelines and other related documents in order to achieve global harmonization, which also facilitates international trade. Behind all this is the protection of consumer health and also the establishment of fair practices in the food chain. It can be said that the Codex seeks international agreement and therefore shows suitable flexibility. It allows individual countries to incorporate Codex standards and guidelines into their own laws and recommendations. This is also the case with the Codex guideline on nutrition labeling. The Codex operates within the framework of committees specialized for certain areas, and the documents drawn up and adopted by it are finalized with the approval of the main committee [15, 16].

3.2. The Codex and nutrition labeling

With regard to nutrition labeling, two specialized committees play key roles, one of which is the Codex Committee on Nutrition and Foods for Special Dietary Uses (CCNFSDU) which, through its activities in this field, contributes, among other things, to the enforcement of scientific and professional basis and to the determination of dietary intake reference values. The other such specialized committee is the Codex Committee for Food Labelling (CCFL), which finalizes the information on nutrient composition related to food labeling in this area. A guide on nutrition and health claims has been developed within the framework of a similar collaboration. One of the objectives of the guidelines is to provide consumers with an understanding of the labels on the products and to provide them with sufficiently detailed information [17].

The basic requirements for nutrition labeling were first defined in guideline CXG 2-1985 in 1985 as a voluntary labeling element (except for food intended for specific groups, for which nutrition labeling was already mandatory at that time and was regulated by a separate standard that is CODEX STAN 146-1985), which applied to both prepacked and non-prepacked foods. The guideline is still being developed and refined to this day, and in this spirit there have been complete revisions in 1993 and 2011, and nine amendments between 2003 and 2017. Initially, nutrition labeling was voluntary, however, with a modification in 2012, it became mandatory for prepacked foods. In 2011, an annex defining the general principles of the Nutrient Reference Values (NRVs) for the population over 36 months of age was added to the guideline, which was revised four times between 2013 and 2017.

At international level, the general guideline for claims (CXG-1-1979) was adopted by the Codex in 1979. The principles of nutrition and health claims were defined for guidance in 1997, supplemented by terms such as „low fat”, „high fat”, etc. (CXG-23-1997).

The Codex guideline makes the data in Table 1 mandatory, but if a nutrition or health claim is made on food, the labeling should be supplemented with the nutrient claimed or the other substances with physiological effects, e.g. caffeine content. When there is a claim related to fatty acids, the amounts of the different fatty acids (saturated, monounsaturated, polyunsaturated) and, where required by member state regulation, the trans fatty acid content, in addition to the mandatory elements. The amounts of vitamins and minerals may be indicated if the product contains significant amounts of them.

It also allows for the voluntary indication of additional nutrients if, for example, required by national regulation, formulated by national recommendations or simply considered to be useful by the producer of the food. In all cases (mandatory, voluntary), the data must be expressed for 100 g weight or 100 ml volume, or portion, and it may be supplemented by the percentage of the Nutrition Reference Value (NRV). Regarding the presentation (font size, order of energy and nutrients, etc.), general principles have been formulated in the recommendations [18].

In addition to consumer education programs, the Codex guideline provides the opportunity to use other forms of voluntary expression through eye-catching graphic elements or symbols. These can help the consumer to get to know and understand the given nutrition declaration, and thus the nutrient content of the food, more easily.

Table 1. Content elements of the nutrition labeling of foods (=mandatory) in various laws

4. Regulation of nutrition labeling in the European Union

4.1. Antecedents of legal harmonization

The basic objective of the regulation of food labeling, and thus of nutrition labeling, is to properly inform the consumer. In 1979, Council Directive 79/112/EEC on the labeling of foodstuffs in the European Union [19, 1], did not yet cover the topic of nutrition labeling. Nutrition labeling was first regulated in 1990 by Council Directive 90/496/EEC as a voluntary labeling option, following the Codex Alimentarius guidelines on nutrition labeling. An exception was the regulation of foods for special dietary uses. At that time, however, it was agreed among food legislators that food business operators, especially small and medium-sized enterprises, should be encouraged to gradually introduce nutrition labeling [20].

Council Directive 90/496/EEC provided two options for nutrition labeling, the elements of which are shown in Table 1. Quantities could be indicated per 100 g weight or 100 ml volume, or per portion, provided that the number of portions in the package was also indicated. There were specific rules for their display: they had to be indicated in a tabular form or in a linear, quantity-by-quantity manner, in a clearly visible way, depending on the space available (at that time, the applicable minimum font size had not yet been determined). Mandatory elements of the label showed the quantities of energy, protein, carbohydrate, fat or energy, protein, carbohydrate, sugars, fat, saturates, dietary fiber and sodium.

The nutrition labeling may have included one or more of the following: starch, polyols, mono-unsaturates, polyunsaturates, cholesterol. Vitamins and minerals present in significant amounts could also be indicated. The annex to the directive also contained the recommended daily allowances for some vitamins and minerals, as well as the definitions of significant amounts (when determining the significant amount, 15% of the recommended intake in this annex should normally be taken into account for each 100 grams, 100 milliliters or one package of the food, if the package contains only one portion). Graphic display was allowed, but special rules were not defined.

The calculation of nuritional value could be based on the results of the tests performed by the food manufacturer, or on calculations based on known or actual average values of the ingredients used, or on calculations based on generally established and accepted data.

Nutrition and health claims appeared more and more frequently on food labels throughout the European Union. Member state regulations were quite diverse, therefore harmonization was necessary, resulting in Regulation (EC) No 1924/2006 on nutrition and health claims made on foods, which specifies which claims (e.g., low energy, energy reduced, source of protein, etc.) may appear on the label and under what conditions. The foods on which a claim is made can have an effect on dietary habits and overall nutrient intake, therefore consumers should be aware of their nutrient content. This goal can be achieved by the mandatory nutrition labeling of such foods [21]. Nutrition labeling is also mandatory in case of addition of vitamins, minerals and certain other substances to foods (Regulation (EC) No 1925/2006).

According to a 2003 study by the DG SANCO (Directorate-General Health & Consumer Protection), 35 to 85% of pre-packaged products in EU member states bore nutrition labeling. The survey pointed out that consumers are interested in nutrition labeling, especially in the case of processed foods, but the majority only requires it, but do not actually use this information.

The results of a consultation in member states in 2003 drew attention to the fact that the voluntary nutrition labeling system was not working satisfactorily and that a legal change was inevitable. Mandatory nutrition labeling was required. The mode of display was particularly important, because the use of small font sizes and multilingual labels made labels confusing, and there was also a need to define exceptions (e.g. packaging materials with small surface areas, non-prepacked products, alcohols etc.). Legislators have recognized that the obligation to provide nutrition labeling may present a problem to businesses because of the additional costs, to which a long transition period and the development of guidelines could be a solution. It was found that alternative nutrition labeling could also be useful, however, if there are too many labeling methods on the market, a great variety can also confuse consumers and the functioning of the internal market. As a result of the survey, the options „Big 4” (energy, protein, carbohydrate, fat) and „Big 8” („Big 4” supplemented by saturated fatty acids, sugar, fiber and salt) were proposed by member states. It was noted that consumers do not understand the indication of the amount of sodium, so it is necessary to use the term salt (table salt). It was also judged that providing the energy content in kJ was not understandable for everyone, therefore the introduction of the use of Kcal was also on the agenda [22, 23].

Regarding the use of other alternative forms of nutrition labeling (in addition to the nutrition labeling) there was a consensus that it should be clear and easy to understand. They also agreed that GDA (guideline daily amount) is a useful and easy to understand form of expression for all stakeholders of the food chain, but it can only be successful if it is harmonized at the EU level and developed by EFSA (European Food Safety Authority) or another independent scientific body [23].

A 2005 consumer survey by BEUC (The European Consumers’ Organisation), conducted in five countries (Germany, Denmark, Spain, Hungary and Poland), showed that nutrition labeling is of paramount importance to respondents; 74 to 84% of those interviewed stated that nutrition labeling was necessary. However, price, date of minimum durability/shelf-life and brand name are the most sought after information, nutrition labeling is read by only a few people, but the amount of fat and portion size are read by 50% of respondents. They spoke in support of other simplified forms of display. They also found that nutrition claims attract consumers’ attention and influence their purchases. 80% of respondents stated that nutrition labeling was easy to find and 70% thought it was easy to understand, while for 50% this information was also reliable. Survey data have shown that the marketing value of claims is markedly high [24].

The Commission’s 2007 White Paper on nutrition, overweight and obesity related health issues noted that the number of overweight and obese people in the European Union, especially children, had risen significantly over the previous three decades. Although the individual is primarily responsible for their own and their children’s lifestyles, it is an indisputable fact that the environment also effects behavior. Also, only a well-informed consumer is able to make rational decisions. Finally, an optimal outcome in this area can only be achieved if the different policy areas (horizontal approach) and the various levels of action (vertical approach) complement each other and are integrated.

It pointed out the need to think about making nutrition labeling mandatory and the regulation of the simplified labeling used on the front side of packaging.

The Commission’s findings in the White Paper, growing consumer interest in the relationship between the diet and health, as well as the need to select a diet that meets the individual’s needs have necessitated the implementation of a nutrition labeling systems that is uniform and mandatory throughout the European Union [25, 26].

EU rules on food labeling, pertaining to all foods, were laid down by Directive 2000/13/EC, most of which reached back to the regulatory principles that emerged in 1978, while Council Directive 90/496/EEC had become obsolete, therefore it was time to amend it [27, 7].

4.2. Legal harmonization

Based on the findings of the White Paper and the results of the surveys, Regulation (EU) No 1169/2011 (hereinafter referred to as: the Regulation) on the provision of food information to consumers, which ensures a high level of consumer protection, the free movement of goods and a level playing field, was established. The Regulation contains detailed rules on the labeling of prepacked foods, but also covers the labeling of non-prepacked foods to some extent. Since mandatory nutrition labeling imposes a significant burden on food business operators, the regulation allowed stakeholders a five-year preparation time, i.e., nutrition labeling on prepacked foods became mandatory from December 13, 2016 [7]. The goal of the legislation was to enable food information to reach the average consumer and to help them make a decision, despite their limited nutritional knowledge, while not creating barriers to trade [22, 25].

Nutrition labeling according to the Regulation must be applied to all foods. Exceptions are food supplements and natural mineral waters. Unlike before, the new type of nutrition labeling prioritizes as mandatory elements energy content and nutrients whose excessive intake carries a health risk. Exceptions to this are carbohydrates and protein, which have become mandatory items due to the increasing frequency of diabetes and the resulting kidney disease. The elements in yellow in Table 2 are mandatory, but it is possible to provide additional elements (marked in blue) on a voluntary basis. Nutrition labeling is also mandatory for the use of nutrition and health claims (on the packaging in the case of prepacked foods, while it does not have to displayed on non-prepacked foods, but the information should be available). Vitamins and minerals present in significant amounts may also be indicated, in accordance with the rules on specific values.

Certain foodstuffs are exempt from labeling in accordance to Annex V to Regulation (EU) No 1169/2011.

Table 2. Mandatory and voluntary elements of nutrition labeling of foods in Regulation (EU) No 1169/2011

The information may be given per 100 g weight or 100 ml volume but may also be expressed per portion or unit of consumption (for specific portion/packaging unit or characteristic unit of consumption due to the nature of the food). The amounts of vitamins and minerals referred to in Part A of Annex XIII to the Regulation should also be expressed as a percentage of the nutrient reference value (NRV) per 100 grams or 100 milliliters of the product. The energy content and the amounts of nutrients may also be expressed as a percentage of the nutrient reference value, expressed per 100 g weight or 100 ml volume, or per portion or unit of consumption. For nutrient reference values expressed per 100 grams 100 milliliters, the following information should also be provided: „Reference intake of an average adult (8400 kJ / 2000 kcal).

In terms of presentation, the Regulation is quite precise and clear; the elements of the nutrition labeling shall be presented in a specific order, preferably in a tabular form (if this is not possible, then continuously, without interruption) following each other, in the same field of vision, in a specific font size. The nutrition labeling is a closed list to which, in the case of foods for normal public consumption, additional elements cannot be added within the list, only to the end of the list (e.g. the amount of lactose should not be included with the sugars, it can only be displayed following the table).

The calculation of nutritional value could be based on the results of the tests performed by the food manufacturer, or on calculations based on known or actual average values of the ingredients, or on calculations based on generally established and accepted data.

Tolerance limits for nutrition labeling are important because, due to the natural variations in the composition of the raw materials and the effects of production and storage, it is not possible to determine the nutrient content of foods accurately within the analytical error.

However, the values given on the label must not deviate from the actual values to such a significant extent as to mislead or harm consumers. In relation to this, a guide has been developed under the coordination of the European Commission to help establish tolerance limits for nutrition values displayed on food labels.

According to the Regulation, specific elements of the nutrition labeling can be repeated in the main field of vision in two ways:

  1. energy, or
  2. energy, fat, saturates, sugars, salt.

In addition to the mandatory display, the Regulation also allows the use of graphic forms and symbols.

There are many voluntary graphic expressions and representations of nutrition labeling in the European Union. These display formats differ from each other. These display categories are not comparable, as they are based on completely different principles and have different uses.

Currently, we can basically distinguish four categories (Table 3).

Table 3. Examples of voluntary nutrition labeling of foods

5. Legal environment in Hungary

From the middle of the 19th century, the authorities of developed European countries began to adopt food laws. The first legal regulation of food in Hungary was Act XLVI of 1895 (on the prohibition of counterfeiting agricultural produce, products and articles) [29].

In the first decades of the 20th century, severe food crises occurred on the continents, from malnutrition to overnutrition. Over time, overeating in Europe started to pose an increasing health risk, leading to obesity and other health disorders. As a result, health organizations in developed and developing countries have become increasingly concerned with the regulated satisfaction of human nutritional needs. They were looking for the amount of energy, protein, fat, vitamins etc. which was absolutely necessary to maintain health, but at the same time they also studied the excessive intake of these nutrients and its consequences.

Starting from 1949, the Institute of Food Science (former name of the National Institute of food and Nutrition Science (OÉTI) regularly examined the diet of the Hungarian population and continuously modified the domestic nutrient requirements and created nutrient tables [30].

Statutory order no. 27 of 1958 was the first legal act that regulated the production and distribution of foods and beverages and ordered the establishment of the Hungarian Food Codex [31]. Nutrition labeling did not appear as such in this order, but the importance of the diet and nutritional health was already emphasized for the „health of our people”. As a result of joining the work of the Codex Committee (1963), the ideas and current issues appeared in food regulation in Hungary as well [32].

As regards nutrition labeling, MÉM decree 25/1976 (VII. 11.) on the implementation of Act IV of 1976 on foodstuffs [33] provided that „…where possible, essential nutrients should also be indicated on the packaging of the food to promote modern nutrition” [34]. The concept of “essential nutrient” was not defined in the decree, however, the nutrient table based on the work of OÉTI and edited by Dr. Róbert Tarján and Dr. Károly Lindner names them: energy content, carbohydrate, protein, fat [30]. At that time, laws did not define every detail and, as a result, individual professional decisions, evaluations and authorizations in connection with the given product played important roles.

Hungary recognized the importance of communicating nutrition labeling to consumers and, accordingly, MÉM decree 25/1976 (VII. 11.) provided the opportunity for voluntary nutrition labeling. During this period, a Codex document on the subject did not yet exist.

Strict rules applied to the fortification of foods with vitamins (e.g. only vitamins that also occurred in the food naturally were allowed to be added as fortification to the food). The name of the vitamin and its amount in the food had to be indicated and, in case of „diet” foods, the amounts of the important nutrients, in addition to the otherwise mandatory general labeling data had to be added.

For certain products/product groups, salt, fat, protein, starch, carbohydrate and energy content were also mentioned in the standards as quality criteria, but their indication was not mandatory in all cases. For example, in case of breads containing whole wheat flour, the carbohydrate content (per 100 g of product) had to be indicated in addition to the energy content (expressed in kJ) (MSZ-08-1377-86).

MÉM (former Ministry of Agriculture and Food) decree 25/1976 (VII. 11.) was replaced in 1988 by MÉM-SZEM (former Ministry of Agriculture and Food – former Ministry of Social Affairs and Health) decree 10/1988 (VI. 30.), which required the mandatory indication of the energy content per 100 grams (cm3) of the product, expressed in kJ, in case of prepacked foods. Among other things, the decree included the main types of „diet foods”, e.g. the categories of energy reduced foods; low energy; energy free; reduced sodium content and low purine, and their criteria. Nutrition labeling was mandatory on these foods, i.e., the energy content and the amounts of the nutrients that provided the energy, as well as the nutrients characteristic to the food and , possibly vitamins had to be indicated. Foods were allowed to be fortified or supplemented with certain vitamins (retinol, calciferol tocopherol, thiamine, pyridoxine, pantothenic acid, folic acid, cobalamin, ascorbic acid) [35].

This regulation provided that the product information sheet and the certificate of analysis must include the nutritional composition of the food „(protein, fat, carbohydrate, etc.) and other characteristics, energy content (per 100 grams or 100 cm3)”. On the packaging of the food had to be indicated: „the name of the food as specified in the standard, manufacturing authorization, product data sheet or other specification (e.g., marketing authorization in case of imported foods), and other mandatory information specified in the relevant standard (e.g., dry matter content, fat content, etc.)”.

Hungary’s application for membership of the European Union, submitted in April 1, 1994, made it necessary to prepare for legal harmonization. Council Directive 90/496/EEC on nutrition labeling was incorporated into Regulation 1-1-90/496 of the Hungarian Food Codex. Joint FM-NM-IKM decree 1/1996 (I. 9.) on foodstuffs stated that the energy content of foods must be given according to the Hungarian Food Codex. In addition to the data required for a given type of food, foods for special dietary uses and foods with claims (today these are called nutrition and health claims) had to bear the nutrition labeling required by the Hungarian Food Codex [36, 37, 38].

Before 1996, about five thousand kinds of food could be bought, but this number increased sevenfold by the turn of the millennium, because in the meantime new requirements, consumer needs and expectations appeared. Food production had shifted towards the manufacture of higher quality foods, and for this reason, as well as in preparation for accession, the creation of a new legal framework became necessary [29].

The elaboration of Act No. LXXXII of 2003 (the fifth Hungarian food act) was necessitated by Hungary’s membership in the European Union. The basic ideas of the law included the protection of the interests and health of consumers, the protection of the environment, and the promotion of fair competition and the free movement of goods [39]. In Hungary, during the preparation period between 1995 and 2004, the regulations of the European Union were gradually adopted into the food act and ministerial decrees, however, with the accession to the EU on May 1, 2004, these transitional legal acts became obsolete [36, 29].

In 2004, Directive 2000/13/EC on the labeling of foodstuffs was transposed in accordance with the specifications of joint FVM-ESzCsM-GKM (former Ministry of Agriculture and Rural Development – Ministry of Health Social and Family Affairs – former Ministry of Economy and Trade Affairs) decree 19/2004 (II. 26.), so the legal harmonization of food labeling had been completed. Nutrition labeling remained a voluntary labeling element (with the exception of foods with claims, fortified foods and foods for special dietary uses) until the entry into force and mandatory application of Regulation (EU) No 1169/2011.

6. Conclusions and the future of nutrition labeling

Nutrition labeling of foods has come a long way in the European Union, creating the opportunity for consumers to enjoy uniform and detailed information in all member states of the Community. EU and national legislators still face a number of challenges. From health and environmental points of view, our current food consumption habits are receiving increasing criticism. Average energy intake, the consumption of sugars, salt and fats remains above recommended levels, while the consumption of whole grains, fruits and vegetables, legumes and nuts is low [40]. The increase in the incidence of overweight and obesity is critical, so this trend needs to be reversed according to the guidance of FAO and WHO, which requires a shift towards a plant-based diet. The consumption of more fruits and vegetables could also reduce the risk of diet-related diseases and, according to some calculation, the environmental impact of the human diet [41]. The regulation of food labeling must therefore continue to follow scientific developments and provide consumers with the information that can form the basis for a balanced and sustainable consumption of food that meets individual needs in the most comprehensible way possible.

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Investigation of the shelf life of fruit yogurts as a function of the treatment of flavoring substances

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Investigation of the shelf life of fruit yogurts as a function of the treatment of flavoring substances

DOI: https://doi.org/10.52091/JFI-2021/1-3-ENG

Received: 2020. September – Accepted: 2020. December

Szerzők

1 Széchenyi István University, Faculty of Food and Agricultural Sciences, Department of Food Science, Mosonmagyaróvár, Hungary

Keywords

yogurt making, microwave irradiation, fruit drying, microbiological parameters, total viable count, yeast count, mold count, Escherichia coli, coliform

1. Summary

Milk and dairy products represent one of the foundations of the human diet because of their valuable ingredients and pleasant sensory properties. The aim of our research was to investigate how different heat treatment processes (microwave irradiation, drying) affect the shelf life of dairy products (yogurt) from a microbiological point of view. In the course of our measurements, the effects of the different heat treatment parameters of the flavoring substances used in the production of the products (apples, bananas) on the microbiological properties of the products and, thus, on their shelf life were investigated. In our experiments, conventional drying (55 °C, 24 hours) and microwave irradiation technology (800 W, 55 °C, 10 min) were used as treatment forms of the additives. Comparisons were made in terms of microbiological parameters (total viable count, yeast/mold count and E. coli/coliform count). Based on our results, we believe that the drying process can ensure microbiological safety in food production if the air circulating in the equipment has adequate hygienic properties. The microwave irradiation technology can be used successfully to inhibit microbes in foods, in this case fruits. However, the same treatment parameters cannot be applied to different fruits.

2. Introduction and literature review

Milk has been a mainstay in the human diet since the beginning of human history. Its useful ingredients have a beneficial effect on a person’s healthy physical and mental development. The ingredients of milk are physiologically beneficial, one of the outstanding features being its high calcium content, therefore it plays a role in the bone formation of developing organisms [1], and it also contains proteins that are important and easy to use for the body. Due to all these properties, dairy products can be considered as staple foods in the human diet. In the food industry, the milk of many farm animals (sheep, goats, cattle) is processed, but in Hungary cow’s milk is consumed in the largest amount.

Yogurt is a dairy product consumed all over the world. Nutrition science professionals believe that this sour milk product has a high nutritional value (a significant part of its lactose content is broken down during fermentation and it has a significant concentration of Ca++) and beneficial bioactive effects (prebiotic ingredients and probiotic bacteria). Natural yogurt is made by adding lactic acid bacteria that induce lactic acid fermentation in the culture medium during their basic physiological activities. Of all products manufactured from milk, yogurt is the most popular worldwide [2].

In the case of fruit yogurt, when dried fruit or dried pieces are added to the yogurt, the dried products tend to absorb some of the free water in the yogurt gel, thus helping to separate the whey of the product during storage [3]. It is also an advantage of adding fruit that, according to some studies [4], the addition of 10 v/v% of fruit significantly improves the physico-chemical properties of the product. The interior of healthy plant tissues does not contain microorganisms, so the primary microbiota of plant raw materials comes mainly from the soil, water, air and, occasionally, from insects or animals. Plant parts developing in the soil (tubers, roots) and in the vicinity of the soil are usually heavily contaminated, their microflora composition is practically identical to that of the soil. Microorganisms are present on fruit surfaces in the amount of roughly 103 to 105 CFU/g, a significant part of which are lactic acid and acetic acid bacteria. However, the largest part of the microbiota is made up of yeasts, the most common of which are Hensaniaspora, Torulaspora, Pichia, Saccharomyces, Candida and Rhodotorula species. Common spoilage microorganisms in fruits include Alternaria, Aspergillus, Fusarium, Monilia and Mucor species. Fruits are excellent culture media for molds, including many mycotoxin-producing ones. Contamination of the raw material and improper storage conditions often also allow the formation of toxic metabolites. For example, patulin, a toxic substance (mycotoxin) produced by Aspergillus and Penicillum fungi, can be detected in moldy fruits (mainly apples and pears) [5].

During the technological processing of fruits, cutting, slicing, chopping and peeling increase the likelihood of cross-contamination from other materials, tools and equipment at different stages of production. In addition, the increased availability of sugars and other nutrients in minimally processed fruits contributes to the change in the microbiota and increases its population [6, 7]. The main factors in the microbiota of the raw material are the hygiene of the surface of the materials used in the production and the processing equipment, as well as the hygiene of the production environment and the food handlers, which determine the microbiological quality and safety of the final product [8, 9, 10]. The authors of a study on minimally processed plant-based foods detected high total aerobic microorganism counts on food contact surfaces, especially on peelers, knives and cutting boards [10]. The same researchers also reported high levels of facultative anaerobic bacteria of the Enterobacteriaceae family on cutting tables and cutting boards [10]. Although washing and other decontamination procedures are used in the manufacturing processes of all processing plants, it is still difficult to achieve a significant reduction in microbial contamination [11]. Favorable conditions for the growth of microorganisms present in fruits and vegetables can also develop during the packaging and storage periods. Lehto et al. discovered a large number of aerobic microorganisms in surface sampling of vegetable processing plants on devices and equipment in contact with already cleaned vegetables, as well as in the air space of storage, processing and packaging rooms [10].

In the food industry, heat treatment processes are the most important determinants of food safety. Heat treatment of milk is necessary to guarantee its microbiological safety by killing pathogenic microorganisms in milk. Several heat treatment methods are used in the food industry. The efficiency of the heat treatment is ensured by strictly defined temperatures and holding times. In addition to the raw materials of the products, it is also important to ensure the appropriate microbiological properties of the additives. „Heat treatment is an operation related to the warming or heating of milk, cream, etc., the objective of which is to reduce the number of or destroy microorganisms” [12]. Heat treatment during milk processing is a general technological step aimed at improving the shelf life of milk by inactivating microorganisms and enzymes. The use of a raw material with a favorable microbiological condition can also improve the texture quality of certain dairy products, such as yogurt [13].

Microwave technology as a heat treatment process is primarily used in households. In the food industry, it can currently only be used reliably in certain areas. The reason for this is that heat transfer is uneven in microwave equipment, and underheated or overheated places develop in the product. In the case of liquids flowing in a pipe, e.g., when treating milk with microwave energy, this can be avoided [5]. where this technique can be used, it is an advantage, as the time of treatments applied to foods can be reduced, thus making the technique economical. In addition to cost-effectiveness, an additional advantage is that the directions of heat and material transport are the same, so that a dry crust that prevents flow is not formed [14]. Areas of application include drying, thawing of frozen meat, tempering, pasteurization, sterilization and prevention of food discoloration [15, 16].

Sterilizing and antimicrobial effects are also attributed to microwave radiation. In Pozar’s experiments [17], this effect could be achieved using a frequency of 2,450 MHz, and in some cases even using a frequency of 915 MHz. Radiation increases the shelf life of foods by killing the microbes in the food and/or inhibiting their growth.

The effect of microwave radiation on microbes has been investigated in a wide variety of foods and food raw materials, especially in meats. The spreading of microwave pasteurization [18] has been facilitated by the fact that its use in foods does not cause significant damage, as opposed to traditional heat transfer methods. The reason for this is the short heat treatment and irradiation times [19, 20].

Compared to the microwave treatment technology, drying is a more traditional method, the essence of which is the extraction of most of the water content from the fruit, less often, from the vegetable, by gentle heat transfer, which leaves behind an intensely flavored concentrate of significantly lower weight and size that the starting material. Thus, microscopic organisms that remain on the dried fruit lose their viability and ability to reproduce due to a lack of available water. Fresh fruits contain 90 to 95% water, which drops below 15% after drying. In this way, spoilage caused by bacteria and molds can be prevented while retaining certain nutrients, roughage and minerals, such as iron. Compared to fresh fruits, dried fruits contain a lot of carbohydrates, fiber and antioxidants, flavonoids, phenolic acids, carotenoids and vitamins in a concentrated form [21, 22].

The food industry produces yogurt products of various compositions, but the technological processes used in their production are almost the same until the inoculation of milk. Milk is usually inoculated with a 2 to 3% starter culture and incubated at 40 to 45 °C. In this temperature range, the desired final acidity is reached in 3 to 4 hours. If a lower temperature (30-37 °C) is used, the operation takes longer (7-8 hours), but in this case excessive acidification of the product can be prevented [23].

The bacterium Streptococcus thermophilus is primarily responsible for the taste, aroma and texture of the yogurt, and is in fact able to ferment pasteurized milk on its own into yogurt, however, in addition to Streptococcus, Lactobacillus bulgaricus is also used for the fermentation to produce the acids that from in the product. S. thermophilus and L. bulgaricus are usually used simultaneously, in a 1:1 ratio, when inoculating the milk (pH 6.6). Their proportion changes as the fermentation progresses [24].

3. Materials and methods

Manufacture of the products

For the manufacture of the products, raw, untreated milk and 2.8% fat UHT drinking milk (Mizo) were used, which were mixed in a 2:1 ratio before making the yogurts. Raw milk was pasteurized on a hot plate at 75 °C for 15 minutes to achieve adequate initial microbiological safety, and then the drinking milk was added. After heat treatment, the temperature of the milk was allowed to drop to 30 °C, it was inoculated with the amount of thermophilic yogurt culture (Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus thermophilus, YF-L812, Chr. Hansen, France) according to the instructions for use, and then it was stirred for 15 minutes to ensure proper homogeneity. The inoculated milk was dispensed into 2 dl plastic yogurt cups and the cups were placed in a 43 °C thermostat (Binder, Germany), where they were allowed to curdle for 7 hours (pH 4.6). After acid curdling, the fruits treated with the following procedures were mixed into the yogurts.

In our experiment, with the exception of the control sample, two types of heat treatment procedures were used for the fruits, microwave irradiation and a conventional heat treatment method (drying).

The samples prepared in the course of the experiment:

  • Sample 1: fruit yogurt with the addition of raw apples (Ny-A)
  • Sample 2: fruit yogurt with the addition of raw bananas (Ny-B)
  • Sample 3: fruit yogurt with the addition of dried apples (A-A)
  • Sample 4: fruit yogurt with the addition of dried bananas (A-B)
  • Sample 5: fruit yogurt with the addition of microwave-treated apples (MH-A)
  • Sample 6: fruit yogurt with the addition of microwave-treated bananas (MH-B)

Raw fruits were processed in a clean, impeccable condition free of bruises and defective parts, at the optimum degree of ripeness.

For the microwave treatment, a MARS 5 (CEM Corporation, USA) microwave digestion oven was used. Diced raw apples and bananas were placed in the sample holder of the microwave apparatus and subjected to microwave irradiation. The energy transfer program on the device was set to heat the fruits to 55 °C when a power of 800 W with 100% efficiency and with a holding time of 10 minutes was applied, in a total of 15 minutes. Temperature detection and control was performed using a sensor (RTP 300) introduced into the sample holder.

The fruits used in the drying process were cut into pieces of equal size (0.5 cm), so that the drying time would be the same, and then they were placed in a drying apparatus for 24 hours at 55 °C. The dried and microwave-treated fruits were then ground. The knives and the grinder were treated with Mikrozid disinfectant before use. 5 grams each of the ground fruits were added to 150 ml of the already prepared yogurt samples. Until further analysis, the yogurt-dried fruit mixtures were stored in a refrigerator at 4 °C.

3.2. Product shelf-life analysis

The product were tested for 4 weeks in terms of shelf life. Analyses were carried out on days 0, 7, 14, 21 and 28. Microbiological properties (total viable count, yeast/mold count, E. coli/coliform count) were tested every week from the time of production. The experiment was carried out with 3 parallel measurements (n=3) on each sampling day, i.e., with 15 samples for each original sample (Ny-A; Ny-B, A-A; A-B; MH-A; MH-B), meaning that a total of 90 samples were processed.

A plate pouring method was used to grow the microorganisms. From food safety and technological hygiene point of view, a total viable count of 105/cm3 is the critical limit for raw milk, because normal pasteurization procedures can still be used with sufficient efficiency at this microbe count.

The determination of the total viable count was carried out on a PC (Plate Count, Biolab) medium, with an incubation time of 72 hours at 30±1 °C [25]. By culturing on a selective medium prescribed in standard MSZ ISO 7954:1999 at 25 °C, yeasts and molds form colonies. YGC agar (Yeast Extract Glucose Chloramphenicol Agar, Biolab) was used for their detection, as prescribed by the standard. This selective medium is suitable for isolating and counting yeasts and filamentous fungi from milk and dairy products. Plates were incubated at 25±1 °C for 48 hours, after which the colonies developed on the plates were counted [26].

Co-determination of the coliform count and the E. coli count can be accomplished using CC agar (ChromoCULT Coliform Agar, Biolab). Differentiation between the colonies is aided by the fact that coliform colonies are salmon red, while the color of E. coli colonies ranges from dark blue to violet. Incubation parameters for E. coli/coliform were 24 hours and 35-37 °C [27].

Our measurement results were plotted using Microsoft Office Excel 2016®. During the evaluation of the microbiological results, microbe counts were displayed in a logarithmic form: the slope values of the lines fitted to each point characterize the exponential growth phase of the microorganisms.

4. Results and evaluation

4.1. Test results of the yogurt samples with apples

4.1.1. Total viable count

According to Figure 1, on days 0, 7 and 14 of the measurement, the total viable count showed almost the same results for the yogurt with dried apples and the yogurt with microwave-treated apples. The yogurt with raw apples already showed higher total viable count values in the second measurement (day 7) compared to the other two samples. Here we already saw a significant difference between the cell counts, which difference only increased over time (day 14). In the case of yogurts with microwave-treated apples and dried apples, the rates of increase in cell counts were approximately the same. This result is also supported by the slope values marked in Figure 1a.

Figure 1. Results of the determination of total viable count in the case of yogurts with apples

4.1.2. Yeast/mold count

Based on Figure 2, it can be concluded that from the first measurement data to the last measurement result, the yogurts with microwave-treated apples and with dried apples showed significantly lower yeast counts than the yogurt with raw apples. There was no difference of the same order of magnitude between samples MH-A and A-A, however, on day 21 of the measurement, there was a clear, significant difference in favor of sample MH-A. Based on this, microwave heat treatment proved to be more effective in inhibiting the activity of yeasts, using the treatment settings applied by us.

In the case of yogurts with apples, it is clear that the microwave technology proved to be the best treatment for both the total viable count and the yeast/mold count. The yogurt with dried fruit exhibited similar cell counts and growth tendencies. However, at the end of the storage time, larger differences between the cell counts developed here. In terms of shelf life, the worst results were obtained for the samples with raw fruit. Differences of an order of magnitude were measured compared to the other two samples, there were significant differences (p≤0.05).

Figure 2. Results of the determination of yeast count in the case of yogurts with apples

On day 21, at the third sampling time, with the exception of yogurts with microwave-treated apples, yogurts with raw and dried apples were spoiled. After the third measurement, in addition to the high yeast count, a significant mold count was also detected in the yogurts with raw or dried apples. In contrast, in yogurts with microwave-treated apples, no mold colonies could be detected after the third measurement.

For the mold count, under the current regulation [27], the level of compliance (m) is 102 CFU/cm3 for fermented milk, dairy products, sour dairy products, cottage cheese and cottage cheese products, while the rejection limit value (M) is 5*103 CFU/cm3.

In terms of mold count, the presence of no molds was detected in yogurts with apples during the first two measurements, on days 0 and 7. On day 14 of the experiment, colonies of mold appeared in the samples with raw and dried apples, already with a value above the rejection limit as defined by the regulation in the case of raw apples (3*104 CFU/cm3). However, in the case of the product with dried apples, the number of mold colonies remained at an acceptable level (2.2*102 CFU/cm3) according to the relevant regulations [27].

4.2. Test results of the yogurt samples with bananas

4.2.1. Total viable count

In the case of yogurts with bananas, it was found that the two types of treatment procedures (drying, microwave) also have an effect on the microbial count. In the case of the microwave-treated sample, the increase in the total viable count was not significant until day 21 of the experiment compared to the initial TVC (Figure 3). On the other hand, the total viable count increased from week to week for the yogurts supplemented with raw or dried fruits. In addition, it was also found that the total viable count of the sample supplemented with dried fruit had the highest total viable count, and the most intense increase in the TVC was also observed in this sample. Already on day 7 of the storage experiment, there were significant differences between the test results of the samples, with an order of magnitude difference between sample A-B and samples Ny-B and MH-B. On day 14 of the experiment, orders of magnitude differences were observed between the date of all three samples. In terms of total viable count, the increase in TVC was the lowest in the case of sample MH-B.

Figure 3. Results of the determination of total viable count in the case of yogurts with bananas

4.2.2. Yeast/mold count

Based on the yeast count results (Figure 4), it was found that there was no significant difference between the yogurts with raw and microwave-treated bananas in terms of the colony counts of the samples and the growth trends of the microorganisms. However, yogurts with dried apple were characterized by a rapid increase in cell number, which also affected the organoleptic properties of the product. From day 7 of the experiment, there were already significant differences between samples MH-B and Ny-B and samples A-B and Ny-B. Microwave treatment proved to be the most effective in this case as well.

The evolution of the mold count during the shelf life was examined also in the case of yogurts with bananas. The results showed that during the first two measurements, on days 0 and 7, no mold colonies developed. However, on day 14 of the experiment, molds appeared in an amount of 1.4*104 CFU/cm3 in the sample with dried bananas, a number which well exceeds the compliance limit value according to the regulation (102 CFU/cm3), moreover, it falls into the rejection category (5*103 CFU/cm3). For the other two samples (raw and microwave-treated bananas), no molds were present on day 14. On day 21 of the experiment, molds also appeared in the yogurt supplemented with raw bananas in an amount of 3.0*101 CFU/cm3, which does not yet exceed the compliance limit value. Mold was still not detectable in sample MH-B. On day 28 of the experiment, the mold count of the yogurt with raw bananas also exceeded the rejection limit value by approximately 1 order of magnitude. No mold could be detected in sample MH-B even on day 28.

The results obtained for dried products suggests that the 24-hour drying with a gentle heat treatment did not sufficiently improve the microbiological condition of the materials used. It can be assumed that the hygienic condition of the air flowing through the drying apparatus was also inadequate. We believe that fruits prepared for yogurt products should only be dried in a room and equipment that has impeccable air, and have exhaust and adequate air filtration systems.

4.2.3. E. coli/coliform results of the yogurts

The bacterium Escherichia coli is the most important microbe in the normal intestinal flora, making it a natural component of the digestive system of all warm-blooded animals and humans. It can enter foods from fruits and vegetables if they had not been cleaned thoroughly enough, but it can also be found in raw milk or dairy products made from it.

Figure 4. Results of the determination of yeast count in the case of yogurts with bananas

Under current regulation [28], the compliance level is (m)<1/CFU/cm3 for fermented milk, dairy products, sour dairy products, cottage cheese and cottage cheese products, while the rejection limit value is (M)<10/CFU/cm3.

During the tests carried out on days 0 and 7 of the experiment, no E. coli bacteria were detected in any of the samples prepared by us. However, on day 21 (week 3) of the experiment, the bacterium became detectable in all yogurts except the samples with raw bananas and microwave-treated bananas. By week 4 of the experiment, E. coli also appeared in the yogurt with raw bananas. Thus, by the end of the study, only the yogurt supplemented with microwave-treated bananas met the legal requirements.

Coliform bacteria are found in wetlands, in soil and on the vegetation, and are usually present in large numbers in the feces of warm-blooded animals.

According to the relevant regulation (EüM decree 4/1998 (XI. 11.) – EÜM: former Ministry of Health Affairs), the compliance level is (m)<10 CFU/cm3 for fermented milk, dairy products, sour dairy products, cottage cheese and cottage cheese products, while the rejection limit value is (M)<102 CFU/cm3.

Coliform bacteria were detected in all samples on day 0 of the experiment, however, the compliance limit value was exceeded only by the results of the yogurt samples supplemented with dried fruits. After 1 week, however, coliforms could only be detected in the yogurt with dried bananas. It is likely that the decrease in the pH value of the yogurt prevented the bacteria from growing and surviving.

At week 3 of the experiment, coliform bacteria were detected in the samples supplemented with raw fruits, they were not present in the other samples. In our case, the samples supplemented with raw fruits reached the M value, so after 21 days the products were not suitable for human consumption.

During the microbiological studies, the determination of Salmonella and Staphylococcus aureus was also performed, as required by the regulation. These tests were negative in all cases.

Schnabel et al. infected raw fruits with seven microbial strains (including the E. coli bacterium also tested by us) with a cell count in the 108 order of magnitude. The samples were then treated with microwave-assisted plasma, which reduced the cell count by 4 orders of magnitude already after 5 minutes of treatment. The treatment was performed under non-thermal conditions (at 30 °C), thus excluding the microbicidal effect of the temperature [29].

Picouet et al. showed that microwave treatments had a similar effect on the E. coli O157: H7 and total viable count values, i.e., a 1.01-1.16 log CFU g-1 decrease was detected. The same treatment parameters greatly affected L. innocua, with population below the detection limit (10 CFU g-1) in most cases. In apple puree samples, the total viable count remained stable during storage at 5 °C, with a slight increase on day 14 [30]. This trend was also observed during our own measurements. Our results confirm that the objective of our research was achieved, which was to verify the microbicidal and inhibitory effect of microwave treatment.

Our results are also supported by the fact that 5 to 25 seconds of microwave treatment (65 °C, 1200 W, 2.45 GHz) can reduce the Salmonella cell count in vegetables by 4 to 5 orders of magnitude, thus confirming the stronger microbicidal effect of microwave treatment compared to other ones [31].

5. Conclusions and recommendations

When using fruits as flavoring agents in yogurts, two types of heat treatment were applied to increase the shelf life of the products. The effect of the microwave treatment method on shelf life was characterized by the length of shelf life after the addition of conventional and untreated fruits to yogurt.

Based on the microbiological studies, it was found that microwave treatment was the most effective of the various heat treatments. Of the methods of heat treatment of fruits, microwave irradiation resulted in a lower total viable count compared to untreated fruits and drying technology.

Based on our microbiological results, we believe that the contamination or texture of the raw fruit fundamentally influences the effectiveness of the treatment. After microwave treatment of bananas, the presence of E. coli could not be detected in the yogurt by the end of our experiments (day 28), as opposed to the treated apples, where its presence was already detected on day 14. This may be noteworthy because the presence of E. coli was not detected in either case on the day the products were prepared. It can be assumed that microwave irradiation exerted a more intense germicidal effect in the softer texture of bananas than in apples which have a harder consistency.

It was found that the drying procedure is suitable for the production of microbiologically safe food if the microbiological condition of the air circulating in the equipment is also adequate.

Based o our results, it is hypothesized that microwave irradiation technology can be applied successfully to foods, in this case fruits, to inhibit microorganisms living inside and on the surface of fruits.

While in the case of yogurts flavored with apples, the microbiological characteristics of samples with raw fruit were worse, in the case of bananas, the drying technology proved to be the most unfavorable from a microbiological point of view. The most likely reason for this may be that, compared to apples, bananas contain on average three times more carbohydrates which became more concentrated as the result of drying. This high carbohydrate fruit mixed with the yogurt may have served as a culture medium for various microorganisms.

In order to determine whether or not the use of different microwave temperatures (other than 55 °C), power and treatment times would lead to better shelf-life results, further tests are required.

Of the heat treatment procedures, microwave may be suitable both for treating milk, thus reducing the number of microbes, and for reducing the total viable count of the flavoring agents (spices, fruits, vegetables) used.

6. Acknowledgment

This publication was supported by project no. EFOP-3.6.1-16-2016-00024 titled „Developments for intelligent specialization in cooperation between the University of Veterinary Medicine and the Faculty of Agricultural and Food Sciences of Széchenyi István University. The project was supported by the European Union and co-financed by the European Social Fund.

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Chronic aflatoxin M1 exposure of Hungarian consumers

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Chronic aflatoxin M1 exposure of Hungarian consumers

DOI: https://doi.org/10.52091/EVIK-2021/2-2-ENG

Received: March 2021 – Accepted: May 2021

Authors

1 National Food Chain Safety Office, System Management and Supervision Directorate
2 University of Veterinary Medicine, Digital Food Chain Education, Research, Development and Innovation Institute
3 University of Debrecen, Doctoral School of Nutrition and Food Sciences

Keywords

deterministic exposure estimation, chronic aflatoxin M1 exposure, consumer groups at risk, carcinogenic effect, risk of liver cancer

1. Summary

The mycotoxin contamination of foods also appears in the food chain. Aflatoxin is metabolized in animals and its aflatoxin M1 (AFM1) metabolite, which is similarly, but ten times less genotoxic and carcinogenic than aflatoxin B1 (AFB1), is also present in milk, liver and eggs. Of these, the most significant food safety risk is posed by the contamination of milk with AFM1. In our article, the deterministic exposure estimation of Hungarian consumers is presented, based on the AFM1 contamination of milk and dairy products. The results indicate that the exposure of children under three years of age clearly poses a health risk, while the exposure of the 3 to 6 year old age group is borderline. The exposure of older age groups in ng/kg body weight does not pose an immediate health risk due to the increasing body weight. However, it needs to be emphasized that the presence of carcinogenic compounds should be kept to a minimum in all age groups. To this end, we propose an amendment to the regulation regarding the factory inspection of milk.

2. Introduction

In our previous paper on mycotoxin contamination, we presented the mycotoxin contamination of foods and feeds, the legal regulation of their tolerable maximum concentrations, the limitations of sampling procedures, and the experiences of current domestic practice were analyzed [1]. Using the data of the food consumption survey conducted in 2009 and measurement results available from the period 2010-2018, the exposure of Hungarian consumers to DON and aflatoxin M1 was estimated. Based on our preliminary estimates, it was determined that for some consumers, exposure to aflatoxin M1 and DON may exceed the toxicological reference values from time to time due to the yearly variation in food contamination levels, which may pose a risk to human health.

In our present paper, the results of our calculations carried out by a deterministic method and using the latest analytical measurement results and the data of the Hungarian food consumption survey performed in 2018-2019 using the methodology uniformly applied in Europe are presented, which provides information on chronic AFM1 exposure of different consumer age groups.

Intensive research is induced by the expected spread of mycotoxin-producing fungi due to global warming, the increase in food and feed contamination levels caused by the toxins they produce and the health problems and economic damages attributed to them. The large number of reports on the results are made more manageable by research area by the regularly published review articles, such as those on the interaction of mycotoxin-producing Aspergillus species with soil microorganisms [2], on human physiological effects of mycotoxin exposure [3], on the application of biocontrol technologies to reduce aflatoxin contamination, on the effect of silage production technology and the microbiota on aflatoxin contamination [4], on the sources, occurrence and regulation of mycotoxin contamination [5, 6] and on its detection methods [7]. A special edition of the journal Frontiers in Microbiology, containing 22 of the latest research articles and summaries, has also been published in the form of a book [8].

In view of the above reviews, only literature publications closely related to the objective of our paper are summarized below.

2.1. Occurrence of aflatoxins

Aflatoxins and other mycotoxins that occur in raw agricultural products (mainly peanuts, maize, rice, nuts, figs, spices and dried fruits) and feeds enter the food chain and can be detected in milk [9, 10, 11, 12], eggs, meat and offal [6, 13]. Aflatoxicol has been detected in the liver, kidney and meat of broiler and laying hens [14, 15]. Compared to the concentration of AFB1 in the feed, >5700-, >4600- and >3800-fold concentrations were measured in the livers of hens, in egg yolk and in egg white, respectively [16].

In addition to the exposure of the animals to AFB1 (µg/kg body weight), the concentration of AFM1 entering the milk from the feed depends on a number of factors, such as the health status of the cow, its milk yield, the lactation period, etc. [17]. The transmission rate is higher in specimens with higher milk yields [18]. The results of several studies have been reported in the literature, according to which the rounded transmission rate varied from 0.35 to 6%. Lower transmission rates (0.08%-0.33%) were observed in sheep [19].

There is less research on the transmission of aflatoxin to the liver, meat and eggs, but these report significantly lower transmission rates compared to milk, making milk still the most significant source of aflatoxin among foods of animal origin [4, 20].

When a food contaminated with AFB1 is consumed, AFM1 is excreted in breast milk to a similar extent as in cow’s milk [21, 22, 23, 24, 25, 26]. Infants and young children who are fed formula or milk drinks based on cow’s milk may also be exposed to AFM1. The results of European surveys indicate much lower levels than African or Asian publications [27].

As with all mycotoxins, aflatoxins show significant annual fluctuations in their levels depending on the weather conditions affecting fungal growth and toxin production [28].

For its most recent risk assessment [29], EFSA used the results of aflatoxin M1 measurements reported by Member States after 2013. Statistical data for some of the major food categories are summarized in Table 1.

Table 1. AFM1 mean and 95th percentile concentration values based on the 2013-2020 Member State data of EFSA.

Comment:

N: number of measurement results; % LCD (left censored data): ratio of results below the detection/quantification limit; P95: 95th percentile; LB: lower bound – result of substitution with the lowest concentration value; UB: upper bound – result of substitution with the highest concentration value; Other: foods for infants and young children.

2.2. Health effects of aflatoxins

Aflatoxins (especially AFB1, AFG1 and AFM1) proved to be extremely potent carcinogenic, kidney and liver damaging, genotoxic, malformative, reproductive capacity decreasing, immunosuppressive and nervous system damaging compounds in all experimental animal species, such as fish, ducks, mice, rats and monkeys [30]. A recently published study showed that the spores of pathogenic fungi cause severe, fatal infections in various birds [31].

High levels of AFB1, both in humans and animals, can cause fast-action, acute poisoning, during which severe hepatitic failure can lead to death, however, human risk of this in developed countries is negligible. Of aflatoxins, aflatoxin B1 is the most potent carcinogenic and genotoxic compound, and it is the one most commonly found in foods and feeds. Most often, it causes hepatocellular carcinoma (HCC), which is why AFB1 has been classified as a Group 1 human carcinogen by the IARC. After consumption of feed contaminated with AFB1, its hydroxy metabolite, aflatoxin M1, which is also a carcinogenic compound, although with a toxicity that is about one tenth of that of AFB1, is excreted by dairy cows in milk [32, 33].

Aflatoxins are rapidly and extensively adsorbed in the small intestine and, once in the liver, the metabolism of aflatoxin is catalyzed by the cytochrome P450 enzyme system found there. AFB1, AFG1 and AFM1 are converted to a reactive electrophilic epoxide that is capable of covalently binding to both DNA and proteins. Glutathion S-transferases (GST) are able to form a conjugative link with the 8,9-exo epoxide of AFB1, which is no longer able to enter harmful reactions in the body, and is excreted through bile and the kidneys. Polymorphisms among individuals result in high variability in enzymatic processes, and thus sensitivity to aflatoxin also varies from individual to individual [30, 34, 35]. In optimal cases, most aflatoxin metabolites are excreted within a few days, however, they have been observed to be present in protein-bound form over a longer period of time (e.g., in the case of aflatoxin-albumin adducts), with a half-life of 30 to 60 days in peripheral circulation [36].

Aflatoxins also damage liver cells directly, as well as indirectly, by altering the expression of genes involved in lipid metabolism. Increased cholesterol, triglyceride and lipoprotein production can cause the disintegration of hepatocytes. Hepatocyte death may lead to acute hepatitis, which can result in liver failure and, in more severe cases death. The disrupted metabolism of hepatitis patients can lead to malnutrition, which indirectly contributes to a general decrease in the antioxidant capacity of hepatocytes, to a loss of liver tissue regeneration capacity and, ultimately, liver failure [3].

Based on the opinion of EFSA experts, a key point in the risk assessment of aflatoxins is the evaluation of the role these toxins play in the development of liver cancer. From this point of view, children are particularly sensitive to aflatoxins, because, due to their low body weight, have a higher intake of food per kg body weight, and the risk of developing liver cancer is also higher in individuals infected with the hepatitis B (or C) virus and in the elderly. In people living in areas where both hepatitis B virus (HBV) infection and aflatoxin exposure are common, hepatocellular carcinoma (HCC) samples show a mutation hotspot (G-T transformation) at codon 249 of the p53 gene, which mutation is considered to be a signature of aflatoxin-induced HCC [37]. The possible reason for this is that hepatitis infection of the liver alters the expression of genes encoding aflatoxin detoxification enzymes, resulting in, for example, the induction of CYP enzymes or a decreased GST activity, thereby preventing the body from adequately eliminating aflatoxins [35]. Due to the immunosuppressive effect of aflatoxins, elderly people with chronic diseases are at particular risk, because in their case the efficiency of cell-level repair mechanisms is inferior, so the elimination of aflatoxins is also less effective. It should be emphasized that aflatoxins are able to cross the placenta, so aflatoxin exposure of pregnant women can also endanger the fetus [38].

2.3. The effect of processing on the aflatoxin content in foods

The common feature of aflatoxins is that they are stable, resistant to processing and heat effects. As a consequence, their presence must also be taken into account in the case of processed foods. Certain processing steps, such as sorting, refining, grinding, cooking, baking, frying in oil, roasting, preservation, flocculating, alkaline cooking, nixtamalisation, extrusion and fermentation, can reduce the concentration of mycotoxins in crops and processed foods, but they are not adequate enough to eliminate all contaminants, so the role of prevention at the very beginning of the food chain is of paramount importance [20]. For example, in terms of AFM1 contamination, it is important to reduce the AFM1 contamination of feeds using pre- and post-harvest biotechnological methods as well as toxin binders [4, 17].

Of heat treatment processes, conventional cooking and baking have little effect on mycotoxin contamination, while methods performed at higher temperatures, or possibly using dry heat [39], are more efficient. The breakdown of mycotoxins is enhanced by the presence of sugars, e.g., glucose, during heat treatment [40].

During the wet milling of cereals, such as corn, aflatoxin is distributed among the milling fractions in the following proportions: soaking water: 39–42%, fiber: 30–38%, gluten: 13–17%, germ, 6–10% and starch: 1%. Thus, the total aflatoxin level in the processed products decreases with the proportion remaining in the soaking water. After the dry milling of corn, the groats, bran and flour fractions contain only 6 to 10% of the original aflatoxin content, with most of the aflatoxin entering the germ and husk fractions [20].

Contamination of rice with aflatoxin most often occurs due to improper harvest and storage conditions. Mycotoxins are found primarily in the rice husk and bran layers. Husked brown rice and white rice obtained by polishing are gradually less contaminated [41].

The various heat treatment processes, pasteurization and freezing do not have a significant effect on the aflatoxin content of milk and dairy products [42]. The reduction effect of some heat treatment processes on AFM1 expressed in numerical values are as follows: pasteurization: 7.6%-12.9%, boiling: 14.5-23.9% [43], UHT treatment: 32% [44].

Different physical and chemical methods have been used with good efficiency to reduce the AFM1 content of milk or other liquid products: microwave irradiation (52%) [43], membrane filtration (81%) [45], biofiltration (81%) [46] combination of centrifugation and filtration (83%) [45], ozone treatment [47], the use of adsorbents (85-90%) [48, 49].

Intensive research is underway on the use of microorganisms. Encouraging results for the reduction of AFM1 contamination in milk have been obtained using Saccharomyces cerevisiae (90-93%) [50], S. cerevisise + L. rhamnosus, L. delbrueckii spp. bulgaricus, B. lactis (100%) [49], the mixture of different yeasts (65-69%) [51], heat-treated L. plantarum (94,5%) [44], L. bulgaricus (58%) [52] and in yogurt using S. thermophilus, L. bulgaricus and L. plantrium strains [53]. It remains to be seen how (in the case of using live microbes) changes in organoleptic properties can be eliminated if non-conventional cultures are used, and how the lactic acid bacterium-AFM1 complex formed can be removed from the product [44].

3. Data used to estimate consumer exposure

Exposure (g/kg body weight or ng/kg body weight) is calculated by multiplying the amount of food consumed (g/kg body weight) and the contaminant concentrations measured in it (ng/kg). In the deterministic method, we multiply the mean (median), or sometimes an upper percentile (95.0, 97.5) value. This calculation results in a point estimate giving a specific value [54, 55]. A more subtle estimate is obtained by probabilistic methods [56, 57], in which the distribution of input data is taken into account and thus a distribution is also obtains for exposure. Care should be taken when considering test results below the limit of quantification (LOQ). If the proportion of samples below the LOQ is between 50 and 80%, a maximum likelihood estimate (MLE) gives the best results [58].

Whichever method is used for the estimation, it is important to take into account the uncertainty of each calculation step, their magnitude, and to evaluate the results obtained in light of their cumulative effect [59]. The calculated uncertainty interval includes the true value with a certain level of confidence, i.e., with a certain degree of certainty [60]. The amount of contaminant entering the consumer’s body (EDI) is compared to the toxicological reference value(s) to determine the expected health risk.

For both short-term and long-term exposure estimation, it is worth examining the consumer groups that are particularly affected by the consumption of the given food/contaminant combination, and comparing the exposure of average consumers and „large consumers” [61].

3.1. Reference values for exposure assessment

Depending on the specific properties of the contaminant, the reference value may be the Acceptable Daily Intake (ADI), the Provisionally Tolerable Weekly/Monthly Intake (PTW/MI) or the Acute Reference Dose (ARfD). For food contaminants, the reference value is usually the tolerable daily intake (TDI). The benchmark dose (BMD) is the smallest dose that is estimated from the fitted dose-response curve at which a preselected effect level (benchmark response – BMR) can be observed, usually an increase or decrease of 5 or 10% compared to the control group. The lower confidence value of the BMD is the BMDL [62]. In the case of aflatoxins, Margin of Exposure (MoE) analysis is used to characterize the risk, as no TDI or other toxicological reference value can be established. In such a case, the value of the BMDL, adjusted by the uncertainty factor, is compared to the estimated exposure. The risk attributed to a contaminant can also be expressed as the ratio of the exposure to other reference values, the Hazard Quotient (HQ) or the Hazard Index (HI), which is the sum of the hazard ratios of substances acting on the same target organ or organ system, usually used for cumulative estimates [63].

EFSA recommends the use of 4 μg/kg body weight/day as the BMDL10 value as a benchmark for AFM1 risk characterization [29]. The results obtained are considered to be of concern below 10,000, with an MoE of 10,000 or greater indicating little risk to public health.

To characterize the risk of AFM1, the safe dose recommended by Kuiper-Goodmann (0.2 ng/kg body weight/day) can also be used to calculate the hazard index (HI), which is a quotient of a tumor-causing dose in 50% of animals and a safety factor of 50,000 [64].

According to the 2018 calculations of the JECFA [20], with an average daily intake of 1 ng/kg body weight AFB1, the probability of developing liver cancer is on average 0.269 per 100,000 persons per year, with the upper limit of the 95% confidence interval of the estimate being 0.562/100,000 persons/year in HBsAg+ (positive for hepatitis B surface antigen) individuals. For HBsAg- (negative for hepatitis B surface antigen) individuals, the mean value was 0.017 cancers/year/100,000 persons, with the upper limit of the 95% confidence interval of the estimate being 0.049/100,000 persons/year. The estimated mean values for AFM1 are one order of magnitude lower: 0.027/100,000 persons for HBsAG+, and 0.002/100,000 persons for HbsAg- individuals [20].

JECFA estimated the risk of hepatocellular carcinoma (HCC) associated with aflatoxin exposure using Equation 1:

Ri = [(PHBV+ × HBV+) + (PHBV− × (1–HBV+))] x AF bevitel (1),

where Ri is the HCC risk for region i, HBV+ is the prevalence of chronic hepatitis B in the study population, PHBV+ is the probability of developing liver cancer in this fraction of the population and PHBV- is the probability of developing liver cancer in the rest of the population.

3.2. Food consumption data

The calculations were performed using data from two representative Hungarian food consumption surveys conducted 10 years apart. The three-day survey of 2009 provided food consumption data for 4,992 individuals for a total of 14,976 consumption days, processed by dietitians and broken down into raw materials for the characterization of food consumption habits [65]. The ratio of milk and dairy product consumption days is shown in Figure 1.

Of the 14,976 consumption days in the 2009 survey, the consumption frequency [%] of milk, sour cream and cream, cheese and kefir or yogurt was 75.2, 52.8, 46.3 and 19.1, respectively.

The 2018-2020 survey was conducted within the framework of EFSA’s Europe-wide EU MENU or “What’s on the table in Europe?” project, in accordance with the recommended, uniform methodology [66, 67]. Participating persons were selected from the households participating in the Hungarian Central Statistical Office Household Budget and Living Conditions survey. During the program, two consumption days of 2,657 individuals between the ages of 1 and 74 were recorded, with the help of dietitians. On the 5,314 consumption days, the consumption frequencies [%] of milk, sour cream and cream, cheese and kefir or yogurt were 96.8, 54, 60.6 and 24. The ratio of milk and dairy product consumption days is shown in Figure 2.

Figure 1. The proportion of milk and dairy product consumption days by food group in the 2009 survey
Figure 2. The proportion of milk and dairy product consumption days by food group in the 2018-2020 survey

The distribution of consumers by age group is shown in Table 2.

Table 2. Age groups of the 2009 and 2018-2020 food consumption surveys and the number and proportion of consumers of dairy products by age group

Changes in the frequency of consumption of milk and various dairy products were compared using the milk and dairy product consumption days of the 2009 and 2018-2020 food consumption surveys. The numbers of consumption days of the different foods were compared to the total consumption days of the given survey (Figure 3). The frequencies of consumption of the different foods during the survey periods are characterized by the figure. Among the food categories studied, the consumption frequency of milk and milk-based desserts increased by more than 20%. The consumption frequency of cheeses shows an increase of 14%. The consumption frequencies of sour milk products (kefir, yogurt, sour cream), cream and flavored milks remained almost constant (with the former increasing slightly and the latter decreasing to a small degree). The consumption frequencies of condensed milk and milk powder has decreased significantly. Overall, it can be stated that the consumption frequencies of milk and dairy products has increased slightly over the last 10 years.

Based on the change in consumption frequencies over 10 years, an increase in aflatoxin exposure could be expected, however, this effect was offset by the change in the amounts consumed. The average consumption in milk equivalent, calculated with the median value of processing and enrichment factors, was 310.7 g/day in 2009, and this value decreased to 295.3 g/day in 2018-2020.

Figure 3. Proportion of consumption days to total consumption days; changes in consumption frequencies of various food groups based on the results of the 2009 and 2018-2020 food consumption surveys

Very little data were available on AFM1 concentrations in processed dairy products, so a database of AFM1 processing and enrichment factors for sour milk products (e.g., kefir, yogurt, sour cream) and various cheeses (hard, semi-hard, soft and processed cheeses, fresh cheeses) was compiled on the basis of the latest literature data, and consumer exposure was calculated with the milk equivalent of the consumed quantities of these products.

3.3. Aflatoxin concentration data

AFM1 analytical data are partly derived from NÉBIH’s 2011-2020 Hungarian monitoring survey (1,288 data). 40% of the samples contained measurable amounts of AFM1. Most of the measurements were performed by and HPLC methods on samples taken from the milk of dairy farms or private producers and, to a small extent, from commercially available mixed milk. In addition to the large number of items exhibiting contamination below the LOQ (60%), there were also items with very high contamination compared to the average. Values above 100 ng/kg were: 110, 122, 141, 149, 150, 190, 238, 240, 252, 260, 292, 376, 513, 740 and 860 ng/kg, respectively. We were unable to check the correctness of the results, but we saw no reason to omit them either, so the full data set was used in our further calculations. Another 1,177 samples were analyzed by January 2021 within the framework of the joint project of the University of Debrecen and NÉBIH („Determining of the short- and long-term aflatoxin exposure of Hungarian consumers in the dairy product chain and establishing risk management measures”). In the latter case, milk samples taken directly from the transport tankers by the staff of the dairy company at the 9 dairy farms participating in the project were analyzed between 2019 and 2021 by the ELISA method in the laboratory of the Instrument Center of the University of Debrecen. In samples with concentrations above the 20 ng/kg „action level”, exact AFM1 concentrations were confirmed by HPLC in the laboratory of NÉBIH. The number of samples with concentrations above the LOQ was 672 (57.1%). In the case of samples with concentrations above 20 ng/kg, the dairy farm was notified and it was recommended that appropriate precautionary measures be taken. As a result of this intervention, it was possible to stop the increase in milk contamination, and the AFM1 contamination of the milk produced was kept below the 50 ng/kg level. Detailed results will be published in the final report of the project.

The number of milk samples examined, broken down by year, is shown in Figure 4.

Figure 4. Yearly test sample numbers from the Hungarian survey of NFCSO (NÉBIH) and from dairy farms participating in the joint project

To refine our estimate and to compensate for the large number of values below the LOQ, instead of the usual LOQ, LOQ=0 and LOQ/2 approximations, the values of concentration data below the LOQ were also taken into account with the values of data generated with the help of a distribution with an element number identical to that of the number of measurement results. To measurement results above the LOQ, different distributions were fitted using the GAMLSS and GAMLSS.dist packages of the R statistical software using maximum likelihood estimation, then we used the parameters describing the goodness of the fit (AIC – Akaike’s Information Criterion, BIC – Bayesian Information Criterion and Global Deviance) to select the distributions that gave the optimal fit. The adequacy of the fits was also evaluated by visual comparison of the histogram made from the data and the distribution obtained, as well as by examining the normality of the differences and using a Q-Q plot. The two best-fit distributions were the two-parameter lognormal (Figure 6) and the four-parameter Box-Cox t-distribution (BCT) (Figure 7), which is suitable for the modeling of slowly decaying, continuously distributed data with positive or negative distortion similar to those of aflatoxins [68, 69]. Exposure calculations were performed with a lognormal distribution generated with the assumption of LOQ=5. The selected distributions were then fitted to the entire AFM1 data set, and the evaluation was performed again. Given that a positive change was observed in the parameters describing the goodness of the fit, the distribution chosen were considered to be acceptable.

Descriptive statistics for AFM1 test results and the fitted distribution are summarized below.

Table 3. Descriptive statistics of AFM1 test results (ng/kg) used for the calculations

The relative frequency distributions of NÉBIH and DE test results are shown in Figure 5.

Figure 5. Relative frequency distribution of the AFM1 contamination of milk samples taken in the NÉBIH monitoring program and in the framework of the DE-NÉBIH cooperation

With the exception of the outstanding NÉBIH measurement result values (indicated with blue-red asterisk) in the 10-15 ng/kg range, the frequency of AFM1 concentrations in the LOQ-70 ng/kg range was very similar in the two series of measurements and this justifies the joint evaluation of the measurement results. The relative frequency of samples containing AFM1 in concentrations above 70 ng/kg was <0.5% in the NÉBIH study.

A limiting factor in the risk assessment of aflatoxins was the lack of contamination data. According to the recommendation of EFSA [29], food categories for which the number of positive samples does not exceed 25 or for which the proportion of samples below the limit of quantification is greater than 80% should be excluded. In terms of AFM1 results, only the testing of milk met this criterion (Table 4), the number of tests for processed dairy products proved to be very small.

Table 4. Number of samples tested and % of >LOQ values

1: Formula: infant formula and other baby food
2: Milk-based food

Figure 6. Lognormal distribution fitted to AFM1 concentration results measured in milk
Figure 7. Box-Cox t-distribution fitted to AFM1 concentration results measured in milk

4. Consumer exposure

In the case of food consumption data, the Observed Individual Means (OIM) method recommended for long-term estimation was used. First, all milk and dairy product consumption data were converted to milk equivalent, using the enrichment and processing factors specific to the given food category (Equations 2 and 3).

The intake of foods e1, ..., ej expressed in g/kg body weight (B) on a given consumption day (n), expressed in milk equivalent is

where

me is the mass (g) of food e on consumption day ni,

F is the processing (e.g., enrichment) factor characteristic of food e,

kg body weight is the body weight of the person belonging to the given consumption day,

where e is the AFM1 concentration in the milk used for the preparation of food e and is the AFM1 concentration in the processed food.

Fe is the value calculated from the min., med. and max. results obtained in the experiments.

By multiplying the amounts consumed in g/kg body weight/day by the average AFM1 concentration (ng/kg) calculated from the values of the fitted distribution functions, the exposure values for each consumption day were obtained (ng/kg body weight/day). The intake values of the 2 (2018-2020 survey) or 3 (2009 survey) consumption days of the participating persons were averaged. The results were aggregated by consumer age group and consumer exposure was calculated using the data from both food consumption surveys.

First, the effects of the minimum (Fmin), median (Fmed) and maximum (Fmax) values of the processing factors on the result of the exposure estimation were examined. The calculation was performed with the fitted lognormal AFM1 mean data, as well as with the mean (EDIátl) and 97.5 percentile (EDI0,975) milk consumption values of the 2018-2020 survey. The results are summarized in Table 5. The table illustrates the differences between the mean calculated using the deterministic method and the 97.5 percentile results, based on the 2018-2020 (EU MENU) survey.

Table 5. Estimated combined daily milk and milk product consumption of the various age groups as a function of dairy product processing factors

Taking into account the minimum-median-maximum values of the processing factors did not notably affect the results. There were significant differences in the mean values of the toddler age group when considering the minimum and median factors, therefore, the values calculated with the median of the processing and enrichment factors are used below to present the different exposure estimation results.

The exposure of the various age groups was calculated based on the P0.05, mean, median P0.975 percentile estimated daily intake values (EDI) of the 2009 food consumption survey, median processing factors and mean AFM1 concentration data. The exposures of the different consumer age groups were compared on the basis of the calculated EDI.

Figure 8. Estimated daily intakes (ng/kg/day) deterministic estimation; Comparison of the P0.025, mean, median and P0.95 EDI values of the different age groups, based on the 2009 consumption data

The estimated daily intake values of the age groups of the 2018-2020 survey show a trend similar to that of the 2009 data (Figure 9).

Figure 9. Estimated daily intakes, deterministic estimation; Comparison of the P0.025, mean, median and P0.95 EDI values of the different age groups, based on the 2009 and 2018-2020 consumption data

Comparing both the mean and the 97.5 percentile estimated daily intake values, it is clear that the exposure of each age group has been found mostly constant over the past 10 years. The only noticeable differences are in the mean values of the toddler age group and the 97.5 percentile values of the children age group, but the differences are not significant. The number of items in the toddler age group in the 2009 survey is very low (90 people) compared to the 2018-2020 survey (482 people). Values calculated with a smaller number of elements are burdened with a greater uncertainty.

4.1. Assessment of consumer exposure

Based on the obtained exposure values, the Margin of Exposure (MoE) approach (Equation 3), the hazard index (HI) (Equation 4) and the probability increase of liver cancer attributable to AFM1 intake were used to assess the risk of the Hungarian population. For the MoE method, the BMDL10 value for AFM1 of 4 μg/kg body weight/day was taken into consideration:

Consumer exposure is considered to be risky if the value of MoE is <10,000. The MoE values of exposure calculated by the deterministic estimation from the consumption data of the 2018-2020 food consumption survey are shown in Table 6.

Table 6. MoE values of average and large consumers (97.5 percentile) by age group

The health risk threshold (10,000) is reached or approached only by the “large consumers” (97.5 percentile) of the toddler and children age groups. In the case of the other age groups, no significant risk can be identified using this risk characterization methodology.

For the calculation of the hazard index, the safe dose recommended by Kuiper-Goodmann [59] was used (0.2 ng/kg body weight):

When calculating the hazard index (HI), the degree of risk is directly proportional to the EDI values and is considered to be of concern when the value is 1 or higher. As an example, the results of deterministic estimates using the consumption data of the 2018-2020 food consumption survey by age group are shown in Table 7.

Table 7. Hazard indices (HI) calculated from the EDI values of the different age groups

Note: HI values that pose a health risk are indicated by bold numbers

HI values calculated from the mean and 97.5 percentile values of the estimated daily intakes of the age groups indicate that the risk from exposure of the adolescent, adult and elderly age groups is not considered to be of concern. However, for toddlers and children, in the case of the 97.5 percentile values (large consumers), exposure is well above levels considered to be safe. One of the most important of the above results is the HI value of 1, characterizing the average intake of toddlers, as it suggests that a significant proportion of this age group is exposed to large amounts of AFM1, which is of great health concern.

Assuming a Hungarian hepatitis B prevalence of 0,7% [70], the incidence of liver cancer associated with aflatoxin was calculated using Equation 1. Calculations were also performed with the mean and upper 95% confidence values for the likelihood of developing liver cancer:

Átlag RMo= [(0,0269 × 0,007) + (0,0017× 0,993)] × EDI,

CI,.95 RMo= [(0,0562 × 0,007) + (0,0049× 0,993)] × EDI.

The values of hepatocellular carcinoma (HCCi) attributable to aflatoxin exposure derived from the mean and 97.5 percentile results of AFM1 exposure values calculated from the consumption data of the 2018-2020 food consumption survey by deterministic estimation (DET) (illness/100,000 persons/year) are summarized by age group in Table 8.

Table 8. Incidence of liver cancer as a function of EDI by age group

The risk of developing HCC is increased many times by aflatoxin exposure in the presence of chromic hepatitis B. As the prevalence of hepatitis B is low in Hungary (and in Europe in general), the increase in HCCi induced by aflatoxin is not high either. Although the numerical value of the estimated incidence of liver cancer proved to be very low, their relative values show in this case as well the high risk of “large consumers” of toddlers and children compared to the other age groups.

5. Situation assessment, recommendations

Chronic exposure to AFM1 calculated by the deterministic method and compared to various reference values consistently indicates that the exposure of children aged 1<3 is the highest in the studied age groups. The lowest exposure values are observed for the oldest age groups. Due to a lack of data, exposure in infants aged <1 could not be studied. However, the correlation is not directly between age and intake amounts, but between the change (typically increase) in body weight of increasingly older age groups and the intake amounts.

Given that the toxicity of aflatoxins primarily poses a health risk to developing organisms, special attention should be paid to reducing their exposure and keeping it to a minimum. However, it should be emphasized that the presence of carcinogenic compounds should be kept to a minimum in all age groups.

The body is burdened not only with the AFM1 contamination of breast milk and other milks or milk-based products, but also with the AFB1 taken with other foods and which is 10 times more toxic than AFM1. Since their mechanism of action is the same, the effects of aflatoxins and AFM1 add up. Therefore, we need to pay attention to the quality of our food and the storage conditions of products with open packaging. Products with a musty smell and traces of mold should not be consumed, even after cooking or baking.

Milks, analyzed during the annual monitoring inspections and exhibiting contamination levels that are 10 to 15 times higher than the maximum tolerable level allowed by the law are also marketed. Particularly at risk are those individuals who regularly consume milk from the same source where the animals are fed aflatoxin-contaminated feed.

To date, there is no routine and industry-wide large scale process that can reliably and completely eliminate the aflatoxin content of foods, so the focus remains on preventing contamination. This is a complex task that requires the involvement of all stakeholders in the food chain, starting with the application of good agricultural practices, and the proper preparation and management of arable land. This is followed by the selection of hybrids resistant to mold, and then a series of measures taken during the harvesting, transport and storage of crops that can prevent the growth of molds (setting appropriate temperature and humidity levels, sorting, peeling and physical treatment of the crops). Last but not least, the appropriate storage and treatment cereals, silage or other processed feed products intended for animal feed, checking their aflatoxin levels and their physical, chemical or biological detoxification, if necessary [4].

The success of prevention and the adequacy of milk shipments can also be checked at the level of dairy farms and dairy plants. With a sampling plan developed for the detection of the aflatoxin M1 content of raw milk and an early warning system, and by applying the 20 ng/kg action threshold already proven to be useful in Italy, an increase in the level of contamination can be predicted effectively. Based on the warning, the dairy farm can prevent the maximum tolerable AFM1 concentration (50 ng/kg) from being reached in accordance with local conditions, for example, by modifying the composition of the feed or by using toxin binders. This can reduce the use of contaminated milk batches in primary and secondary milk processing and, consequently, reduce consumer exposure [1, 10, 71].

It should also be noted that, due to the uncertainty of the detection, ELISA kits set to indicate an AFM1 concentration of 50 ng/kg may still classify batches of milk with contaminations of ≤ 65-70 ng/kg as adequate in 50% of the cases.

In order to protect infants and young children who are most exposed to AFM1 and who are also the most vulnerable, but also to protect the health of the entire population, it is recommended that the regulation of dairy plant inspections is amended in a way that ensures that if the AFM1 contamination of the milk delivered from a farm is ≥20 ng/kg, the plant is obligated to notify the dairy farm and NÉBIH, and thereafter to monitor the effectiveness of the dairy farm measures taken to reduce the contamination by daily monitoring of the contamination of the milk delivered from the farm.

It is also recommended that the warning level used in the self-inspection of dairy farms is set to 20 ng/kg instead of the current 50 ng/kg. ELISA kits for the detection of AFM1 at a concentration of 5-10 ng/kg are available for both dairy plant and producer monitoring, so there is no methodological obstacle to the establishment of a new warning threshold.

6. Acknowledgment

The authors would like to thank to Judit Sali and Katalin Csizmadia for providing the relevant data of the 2018-2020 NÉBIH consumption survey, the staff of the DE Instrument Center and NÉBIH for the AFM1 analysis of the milk samples, Attila Nagy and Gabriella Miklós of NÉBIH for their useful suggestions, Béla Béri, who participated in the DE-NÉBIH project, and to the managers and employees of Alföldi Tej and the dairy farms for their cooperation.

Our research program was supported by the National Research, Development and Innovation Fund in the 2018-1.2.1-NKP foundation system with the designation 2018-1.2.1-NKP-2018-00002 (AA, KK).

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The effect of lactation number and lactation stage on milk yield, on the composition and on the microbiological properties of raw cow milk in a Hungarian dairy farm

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The effect of lactation number and lactation stage on milk yield, on the composition and on the microbiological properties of raw cow milk in a Hungarian dairy farm

DOI: https://doi.org/10.52091/EVIK-2021/2-3-ENG

Submitted: July 2020 – Accepted: November 2020

Authors

1 University of Debrecen, Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Food Science
2 University of Debrecen, Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Animal Science, Biotechnology and Environmental Protection, not independent Faculty of Animal Husbandry
3 University of Debrecen, Doctoral School of Animal Science

Keywords

lactation number, lactation stage, cow’s milk, milk quantity, milk composition, microbiology

1. Summary

Changes in the composition and hygienic properties of milk affect producer price, so it is essential for the responsible dairy farmer to collect information on changes in these parameters due to various factors. In their study, the authors seek to answer the question whether there is a fluctuation in the daily milk yield of cows and in the composition (fat and protein content) and microbiological properties (somatic cell count, total plate count, coliform and S. aureus count) of raw cow’s milk in primiparous and multiparous cows or at different stages of their lactation. Based on the data of a Hungarian large-scale dairy farm, it was found that there was no difference in the fat and protein content of the milk, but the daily milk yield was higher in the case of multiparous cows and, compared to the milk of primiparous cows, their milk had a higher somatic cell count and larger amounts of coliform bacteria. The daily milk yield decreased in the successive stages of lactation, but the fat and protein content of the milk increased, which is presumably due to the concentrating effect of the decreasing milk yield. No significant change was observed in the colony count of microorganisms at the different stages of lactation.

2. Introduction

Cow’s milk has a high nutritional value; it contains fats, proteins, carbohydrates, vitamins and minerals, among other things [1]. Examination of the composition of milk is a routine practice on dairy farms for monitoring the hygiene, nutritional and health aspects of dairy cows [2]. The composition of milk can be influenced by a number of factors, such as the number of lactations, the stage of lactation, the season and the feeding technology [3, 4, 5]. Thus, the composition of milk may vary during lactation, and as a result of the interactions of different environmental factors, there may be differences between the different dairy farms [6]. According to Dürr et al., the creation of databases in order to determine the causes and consequences of differences in milk yield and milk composition is of paramount importance. A database should also include records related to these parameters, as well as to lactation-related events and to the individual cows [7].

Due to its nutritional value, high water activity and neutral pH, milk serves as an excellent medium for various microorganisms, which may include pathogenic organisms such as Campylobacter jejuni, Salmonella spp., Staphylococcus aureus, Listeria monocytogenes, Yersinia enterocolitica, etc. [8, 9]. During the primary infection of milk, sick animals themselves are the sources of infection. In case of dairy animals suffering from a systemic disease accompanied by the spread of pathogens, the pathogens may be excreted in the milk. During secondary contamination, the contamination of milk is of environmental origin. Due to improper milking hygiene, milk can be contaminated by the faeces of the animals or the equipment used in milking (milking machines, milk lines, milk storage tanks), among other things [10]. As the milk enters the teat cistern and then the teat canal, it can become infected with various microorganisms, of environmental origin, so the bacterial infection of the first milk jets is remarkably high. At the beginning of milking, it is therefore advisable to separate the first jets of milk from milk that is milked subsequently, and to ensure that they are destroyed [11]. To reduce the number of bacteria in milk, heat treatment is most commonly used. The initial microbiological state of the milk is not only important from a food safety point of view, but can also affect the quality of the dairy products made from it [12].

Coliform bacteria can cause mastitis in dairy animals. Mastitis caused by coliform bacteria can reduce milk production in dairy animals, causing economic losses to dairy farms [13]. The presence of these bacteria in the environment is common, so their presence in food may indicate environmental contamination [14, 15].

Raw milk can be contaminated with S. aureus from a number of sources, such as the environment, milkers’ hands, milking equipment, and so on [16]. The economic damage due to mastitis caused by S. aureus comes from a decrease in the quantity and quality of the milk produced, an increase in the number of somatic cells measured in it, a reduction in the purchase price of lower quality milk, which also leads to a decrease in the turnover of dairy farmers [17]. Prevention is an effective means of controlling S. aureus infection. It can be prevented by adhering to appropriate housing and milking technologies, more frequent fly extermination, pre- and post-disinfection of the teats, the use of disposable udder wipers and regular laboratory testing [18].

External and internal factors can affect the composition of milk, as well as the microbiological state of raw milk. The latter is mostly determined by the hygienic condition of the surfaces that come into direct contact with the milk [19]. Research by Peles et al. found that different husbandry and milking methods at dairy farms with different cow numbers had influencing effects on the microbiological quality of milk [20]. Tessema sought to find a correlation between breed, the age of the animals, the number of lactations and the stage of lactation, as well as the likelihood of S. aureus occurrence.

In his study, he found a significant difference in the prevalence of S. aureus in milk for the two cattle species studied. S. aureus was found in higher proportion of crossbred cows and in older individuals [21]. Examining the milk of different varieties, Bytyqi et al. found no difference in the colony counts obtained [22].

Although fewer Hungarian studies have been conducted on the subject, several international publication have examined whether the number of lactations and the stage of lactation have an influence on the daily milk volume of the animals, the composition of the milk and its microbiological parameters. According to Tessema, there is a significant difference in the prevalence of S. aureus, depending on how many lactations the animals have been through. In his study, he found that S. aureus was more common in the milk of cows that had calved more than twice and produced a positive California Mastitis Test [21]. Tenhagen et al. also found that the incidence of S. aureus increases with the age of the animals [23]. This may be related to the fact that the milking machine may damage the teats during milking, allowing microorganisms to enter the udder from the environment [24]. Another possible reason is that the health status of dairy animals may deteriorate with advancing age, which may have an adverse effect on the somatic cell count of milk [25].

The objective of our study was to determine at a Hungarian large-scale dairy farm whether there is a difference in the daily milk yield of primiparous and multiparous cows, as well as cows at different stages of lactation, and also in the composition (fat and protein content) and microbiological parameters (somatic cell count, total plate count, coliform and S. aureus count) of primiparous and multiparous cows, as well as cows at different stages of lactation.

3. Materials and methods

3.1. Place and time of sampling

A dairy farm in Hajdú-Bihar county (Hungary) was the site of our studies. 440–450 Holstein-Friesian cows are milked on the farm. The farm uses deep litter livestock-keeping and monodiet feeding. Milking takes place in a milking parlor, and no post-disinfection is carried out after milking.

The data on the daily amount of milk, fat and protein content and somatic cell count used for the calculations are derived from the milking results, i.e., from the examination results of the milk samples collected monthly by the Állattenyésztési Teljesítményvizsgáló Kft. All milking results of 38 individuals between May 2015 and January 2020 were used in the calculations. During the calculations, data on the first lactation of the cows (n=387), data on the 2nd to 5th lactation of the cows (n=446), as well as data on the early (under 100 days; n=275), middle (100 to 200 days; n=249) and late (over 200 days; n=309) stages of the lactation of the cows were summarized for the abovementioned time period.

Microbiological tests were performed between May 2018 and October 2019. A total of 62 milk samples were taken from 38 randomly selected, clinically healthy individuals for the microbiological tests. According to which lactation cycle the animals were in and in which stage of lactation during the sampling, the samples taken from the individual cows were classified as follows: 26 samples were taken from 15 primiparous cows and 36 samples were taken from 23 multiparous (2 to 5 calvings) Holstein-Friesian cows. Of the total 62 samples taken, 23 were taken in the early stage of lactation, 21 samples in the middle stage of lactation and 18 samples were taken from cows in the late stage of lactation.

Following pre-disinfection of the teats, wiping them dry using paper towels and milking of the first milk jets, samples were taken from all four udder quarters into sealable sterile plastic sampling vessels with a capacity of 50 ml. The vessels were transported in coolers to the microbiology laboratory of the Institute of Food Science of the University of Debrecen within two hours of sampling. Samples were processed within 24 hours of sampling.

3.2. Microbiological tests

Preparation of the milk samples and the subsequent microbiological tests were performed according to the procedure described by Petróczki et al. [26]. Sample preparation was carried out according to standard MSZ EN ISO 6887-1:2017 [27], the samples were stored at 4 °C until the beginning of the analysis, and they were homogenized by shaking before processing. To prepare the dilution series, peptone water was used which was prepared by dissolving 8.5 g of sodium chloride (VWR International Kft., Hungary) and 1.0 g of peptone (Merck Kft., Hungary) in 1,000 ml of distilled water.

After weighing the appropriate amounts (9 ml each) into test tubes, the diluent was sterilized in a pressure cooker at 120 °C for 30 minutes, then it was cooled and the decimal dilution series was prepared.

The total plate count was determined according to standard MSZ EN ISO 4833-1:2014 [28], which prescribes the use of a tryptone-glucose-yeast agar (Plate Count Agar, PCA) culture medium (Biolab Zrt., Hungary) supplemented with milk powder. After performing the prescribed plate casting method, the plates were incubated at 30 °C for 72 hours.

The amount of coliform bacteria was determined by the plate casting method according to standard ISO 4832:2006 [29], using sterile crystal violet-bile-lactose agar (Violet Red Bile Lactose, VRBL, Biolab Zrt., Hungary). The incubation period was 24 hours at 30 °C.

The determination of S. aureus was performed according to standard MSZ EN ISO 6888-1:2008 [30] by the spreading method, for which Baird-Parker agar (BPA, Biolab Zrt., Hungary) supplemented with egg yolk-tellurite emulsion (LAB-KA Kft., Hungary). The incubation period was 48 hours at 37 °C. S. aureus was isolated from other Staphylococcus species using a latex agglutination test (Prolex Staph Xtra Kit, Ferol Kft., Hungary).

3.3. Statistical analysis

For the analysis of our experimental results, for the calculation of descriptive statistics, for the logarithmic transformation of the amount of microorganisms and for performing t-tests and analysis of variance, the SPSS v.22.0 [31] software was used.

In the case of lactation number, variables were compared using unpaired t-tests and non-parametric Mann-Whitney tests, while in the case of lactation stage, the comparison was performed by one factor analysis of variance and non-parametric Kruskal-Wallis tests. Since the total plate count, the coliform count and the somatic cell count were not found to be normally distributed variables in several cases, a logarithmic transformation was used for these parameters. During statistical analyses, a value of P<0.05 was considered to indicate a significant difference.

4. Results

4.1. Effect of lactation number on milk yield, raw milk composition and its microbiological parameters

Mean and standard deviation values of the daily milk yield of primiparous and multiparous cows, as well as those of the fat and protein content, somatic cell count, total plate count, coliform and S. aureus count of the milk produced by them are shown in Table 1. Cows selected for the study were classified into groups based on the number of lactations (and calvings) into groups of primiparous and multiparous individuals. The average milk yield was 25.67 kg/day for primiparous cows, while in the case of multiparous cows it was 31.04 kg/day. The difference is significant (P<0.05), which means that our experiments confirmed the findings of Bondan et al. and Yang et al., that the daily amount of milk produced by multiparous cows is higher than that of primiparous cows [5, 32]. In the case of crossbred Holstein-Friesian cows, Gurmessa and Melaku studied, inter alia, the effect of calving number on milk yield, but found no difference between the daily milk yield of primiparous and multiparous cows [33]. Pratap and his research group also found no difference in the average daily milk yield of primiparous and multiparous cows (6.43±1.39 and 5.89±2.37 l/day, respectively) [34].

The average fat content of milk during the first lactation of cows selected during our research was 3.74±0.40%, while the average fat content of milk samples taken during the second or later lactation was 3.75±0.36%. The difference was not found to be significant (P>0.05). Similarly to our own results, no difference was found between the average fat content of the milk of primiparous and multiparous crossbred Holstein-Friesian cows by Gurmessa and Melaku, or Pratap et al. [33, 34]. On the other hand, it was found by Bondan and his group that the number of lactations did have an effect on the fat content of milk in Holstein-Friesian cows. While the fat content was 3.47±0.67% in the case of cows lactating for the first time, it was 3.43±0.68% and 3.41±0.67% for cows lactating for the 2nd or 3rd time and for cows that had calved at least 4 times, respectively [5]. In their study in Sudan, Shuiep and his working group examined changes in the fat content of milk with the number of lactations in local and crossbred cows. In the case of local cows, cows in their fourth lactation period had a lower milk fat content (4.82±0.55%) than cows with fewer calvings (1:5.16±0.32; 2:5.22±0.34; 3:5.14±0.34). No difference was observed in the case of crossbred cows [6]. In contrast, Yang et al. found in their research that Holstein-Friesian cows lactating for the first time had a lower milk fat content (3.88%) [32]. Based on the varied results of our study and other literature references, we came to the conclusion that the fat content of milk may be influenced by other factors besides the lactation number.

During the first lactation of the Holstein-Friesian cows selected by us, the average protein content of the milk was 3.24±0.19%, while the average protein content of milk samples taken during the second or later lactation was 3.31±0.16%, with the difference being not significant (P>0.05). This is in line with the findings of Gurmessa and Melaku, as well as those of Pratap et al., as these authors did not find any difference between the average protein content of the milk of primiparous and multiparous cows [33, 34]. In contrast, the research group of Bondan found that the lactation number affected the protein content of milk in Holstein-Friesian cows. While the protein content was 3.24±0.37% in the case of cows lactating for the first time, it was 3.23±0.38% and 3.19±0.37% in the case of cows lactating for the 2nd or 3rd time and for cows that had calved at least 4 times, respectively [5]. Changes in the protein content of milk with the lactation number was also studied by the research group of Shuiep. In the case of local cows, the protein content of milk was lower in the case of cows in their fourth lactation period (3.67±0.19%), than in the case of cows with fewer calvings (1:4.01±0.11; 2:3.82±0.12; 3:3.84±0.12). No difference was observed in crossbred cows [6].

According to our results, the average somatic cell count [242.2×103 (5.12±0.42 lg) cell/ml] in the milk of primiparous cows is less (P<0.05) then in the milk of multiparous cows [356.3×103 (5.39±0.39 lg) cell/ml]. In none of the cases did the averages exceed the limit value [M=400.0×103 (5.60 lg) cfu/ml] laid down in Regulation (EC) No 853/2004 [35]. The results obtained at the Hungarian dairy farm are in line with relevant literature data. According to Mikó et al., as the lactation number increases, the somatic cell count of milk may also increase [25]. This finding was also found to be true by Yang and his group [32]. Sheldrake et al. observed a significant relationship between the calving number and the somatic cell count. They found that with the increase in calving number, there was a smaller change in the udder quarters of healthy animals, however, in the case of udder quarters infected with S. aureus, the somatic cell count increased significantly [36]. The research team of Bondan found that as the lactation number of the Holstein cows studied increased, so did the somatic cell count in milk. While the average somatic cell count in primiparous cows was 4.83±1.73 lg cell/ml, it was 5.31±1.72 és 5.84±1.62 lg cell/ml in cows that had calved 2 or 3 times and cows that calved 4 or more times, respectively [5].

In the milk samples taken from Holstein-Friesian cows that only calved once, i.e., cows in their first lactation period, the average total plate count was 5.1×103 (3.36±0.58 lg) cfu/ml, while it was 4.6×103 (3.30±0.59 lg) cfu/ml in the milk samples taken from multiparous cows, however, the difference was not significant (P>0.05).

However, the average coliform count [1.1×103 (1.35±1.20 lg) cfu/ml] in the milk of multiparous cows was more (P<0.05) than the average coliform count [1.1×101 (0.65±0.61) lg) cfu/ml] measured in the milk of primiparous cows. Tenhagen et al. also included clinically healthy cows in their study, which found that although coliform bacteria were found in higher quantities in the milk of multiparous cows, the difference was not significant [23].

S. aureus was present in only one of the 26 milk samples taken from primiparous cows, in an amount of 5.0×101 (1.70 lg) cfu/ml, while it could be detected in eight of the 36 milk samples taken from multiparous cows. The mean S. aureus count in these samples was 1.5×102 (1.92±0.56 lg) cfu/ml. The values do not exceed the limit value [M=5.0×102 (2.70 lg) cfu/ml] specified by EüM decree 4/1998 (XI. 11.) [37]. In the case of the individuals in whose milk S. aureus could be detected during the microbiological tests, the average somatic cell count was between 44.3×103 (4.65 lg) cell/ml and 357.2×103 (5.55 lg) cell/ml on the basis of milking data. In his study, Tessema also found that the prevalence of S. aureus was higher in the case of multiparous cows (i.e., who had calved more than twice) (who produced a positive California Mastitis Test) [21]. According to Tenhagen et al., the incidence of S. aureus increases with the age and lactation stage of the animals [23].

Table 1. Milk yield of primiparous and multiparous cows and compositional and microbiological properties of milk collected from them

a, b The values marked with different letters in the same rows of the table differ significantly (P<0.05)

4.2. Effect of lactation stage on milk yield, raw milk composition and its microbiological parameters

Mean and standard deviation values of the daily milk yield of cows in the early, middle and late stages of lactation, as well as those of the fat and protein content, somatic cell count, total plate count, coliform and S. aureus count of the milk produced by them are shown in Table 2. The cows selected for the study were classified into groups of early, middle and late lactation stage individuals, based in their stage of lactation. When examining the changes in the daily milk yield of the individuals with the stage of lactation, the finding in the literature that the daily milk yield decreases towards the end of lactation was confirmed. While the average daily milk yield of cows in the early stage of their lactation was 32.10±4.73 kg/day, that of cows in the middle stage of their lactation was 29.08±5.09 kg/day, while that of cows in the late stage of lactation was 23.36±3.63 kg/day. The difference was significant (P<0.05). Bondan and his group came to a similar conclusion in their study. The average milk yield between days 6 and 60 of the lactation of the Holstein-Friesian cows studied by them was 29.4±8.72 l/cow/day; for cows between days 61 and 120 of their lactation, it was 29.2±8.66 l/cow/day; for cows between lactation days 121 and 220, the milk yield was 26.2±8.01 l/cow/day, and at the end of the lactation (>220 days), it was 22.0±7.49 l/cow/day [5]. Gurmessa and Melaku, as well as Pratap et al. also found that the daily milk yield of the animals was higher at the beginning of lactation (6.81±1.45 l/day) than at the end of lactation (5.48±0.05 l/day). In their studies, the daily milk yield of crossbred Holstein-Friesian cows in the middle stage of their lactation was the highest (7.17±0.05 liter) [33, 34]. According to Auldist et al., the effect of lactation stage on milk yield (e.g., a decrease) may be due to a change in the number and activity of the secretory cells within the mammary gland because of physiological reasons [2].

The fat content of the milk of the individuals studied shows a change as we progress towards the end of lactation. The average fat content of cows in the early and middle stages of lactation was 3.65±0.43% and 3.59±0.41%, respectively, which were lower (P<0.05) than the fat content of the milk of cows in the late stage of lactation (3.99±0.47%). The increase in milk fat concentration at the end of lactation may be associated with a decrease in milk yield as lactation progresses, as a decrease in milk yield may have a concentrating effect on milk composition [2]. The fat content of milk also varies at the three stages of lactation according to the publication of Gurmessa and Melaku. For cows in the early or late stages of lactation, the average fat content of the milk (4.46±1.44% and 4.46±1.44%, respectively) was significantly higher than that of cows in the middle stage of lactation (3.70±0.89%) [33]. The publication of Bondan et al. states that the fat content of milk at the end of lactation (>200 days) is higher (3.55±0.67%) than at earlier stages of lactation. However, they also found that the measured average fat content (3.30±0.66%) was lowest between days 61 and 120 of the lactation of the cows. Between days 6 and 60, and between days 121 and 220, average fat contents of 3.40±0.65% and 3.40±0.66% were measured, respectively [5]. Shuiep et al. studied changes in the fat content of milk of local and crossbred cows in Sudan with lactation stage. In the case of the local breed, there was no difference in the fat content between the early (5.31±0.51%), middle (4.67±1.56%) and late (5.28±0.75%) stages of lactation. However, in the case of crossbred cows, the fat content was higher at the end of lactation (4.45±1.43%) than at the beginning of the lactation (3.41±1.09%) or in the middle stage of lactation (3.33±1.05%) [6].

Similar to the fat content, a change in protein content can also be observed as we progress towards the end of lactation. The average protein content measured at the beginning of lactation was 3.08±0.15%, it was 3.20±0.19% in the middle of lactation and 3.56±0.20% at the end of lactation, the difference being significant (P<0.05). Bondan et al. came to a similar conclusion: a higher protein content (3.41±0.36%) was measured at the end of lactation (>200 days) than at earlier stages of lactation. It was also found that the measured average protein content (3.03±0.31%) was the lowest between days 61 and 120 of the lactation of the cows. The measured average protein content between days 6 and 60 of the lactation and between days 121 and 220 were 3.05±0.36% and 3.18±0.32%, respectively [5]. Gurmessa and Melaku, as well as the research group of Pratap did not find any difference in protein content between cows in the early (3.55±1.43%), middle (3.17±0.15%) and late (3.33±0.16%) stages of their lactation [33, 34]. In the case of local and crossbred cows in Sudan, changes in milk protein content with the lactation stage were investigated by Shuiep et al. In the case of the local breed, the protein content of the milk of the animals was higher at the beginning (3.87±0.52%) and in the middle (3.91±0.18%) of lactation than at the end of lactation (3.67±0.17%). In the case of crossbred cows, there was no difference in protein content at the beginning (3.67±0.17%), middle (3.69±0.16%) and end (3.63±0.22%) of lactation [6].

The average somatic cell count in the early stage of lactation of the cows selected for our research was 195.1×103 (5.07±0.43 lg) cell/ml, it was 370.6×103 (5.28±0.50 lg) cell/ml in the middle of lactation, and it was 336.4×103 (5.33±0.41 lg) cell/ml in the late stage of lactation. The somatic cell count was higher (P<0.05) in the milk of individuals in the late stage of lactation than in the case of cows at the beginning of lactation. As lactation progresses, somatic cell counts also show an increasing trend according to Bondan et al. While in the milk of Holstein-Friesian cows between days 6 and 60 of their lactation the average somatic cell count was 4.79±1.90 lg cell/ml, between days 61 and 120 it was 4.89±1.90 lg cell/ml, between days 121 and 220 it was 5.21±1.75 lg cell/ml, and in the case of lactations lasting more than 220 days, this parameter was 5.53±1.53 lg cell/ml in the milk of the cows studied [5].

Total plate counts were also determined during the different stages of lactation of Holstein-Friesian cows. At the beginning of lactation, the average total plate count was 6.8×103 (3.42±0.67 lg) cfu/ml, in the middle of lactation it was 4.4×103 (3.39±0.46 lg) cfu/ml, while at the end of lactation it was 2.7×103 (3.13±0.56 lg) cfu/ml. No significant difference was found between the total plate count values obtained (P>0.05).

Regarding the number of coliform bacteria, the highest count [1.3×103 (1.30±1.23 lg) cfu/ml] was measured in samples taken at the beginning of the lactation of the cows, while the lowest average coliform count [2.2×101 (0.76±0.79 lg) cfu/ml] was detected in samples taken in the middle stage of lactation. The average coliform count in the samples taken at the end of lactation was 1.50×102 (0.90±0.94 lg) cfu/ml. There was no significant difference between the results (P>0.05).

S. aureus was present in a total of 9 samples (14.52%) out of 62 individual milk samples, with an average count of 1.4×102 (1.89±0.53 lg) cfu/ml. In the six samples (9.68%) taken from cows in the early stage of lactation, the average S. aureus count was 1.2×102 (1.81±0.56 lg) cfu/ml, for the single animal in the middle of lactation (1.61%) the S. aureus count was 8.2×101 (1.91 lg) cfu/ml, while for the two cows at the end of lactation (3.23%) it was 2.4×102 (2.15±0.70 lg) cfu/ml.

Table 2. Milk yield of cows in their early, mid and late lactation stages and compositional and microbiological properties of milk from them

a, b, c The values marked with different letters in the same rows of the table differ significantly (P<0.05)

5. Conclusions

In the course of our research work in a Hungarian dairy farm, it was proven that, under large-scale husbandry conditions, for primiparous cows the average daily milk yield is significantly lower (P<0.05) than in the case of multiparous cows. This is probably due to the fact that cows in their first lactation need more amino acids and fat to develop their bodies compared to animals that have already undergone several lactation periods [38].

Regarding the fat and protein content of the milk, there was no significant difference between primiparous and multiparous cows. Since one can find in the literature findings that both support and contradict our results, we assume that the fat and protein contents of milk are also influenced by other factors (e.g., breed, season, etc.), but mapping out of these factors was not the objective of our study. For example, Shuiep et al. reported in their paper different results for two different cattle breeds when examining the changes in the fat and protein contents of milk with the lactation number [6].

Based on our measurement results, we found that the somatic cell count, as well as the coliform count and the S. aureus count were significantly higher (P<0.05) in the milk samples taken from multiparous cows compared to the milk samples taken from primiparous cows. The reason why microorganisms can be measured in larger amounts in milk samples taken from multiparous cows is probably that the teats may have been damaged during the lactations (due to, for example, the milking machine), which may have increased the chances of microorganisms entering the udder [24]. Another reason may be that as the age progressed or the number of lactations increased, the condition of the dairy animals may have deteriorated, which may have had an adverse effect, for example, on the somatic cell count of the milk [25].

It was also proven that, in the stages of lactation, a decreasing trend in daily milk yield can be observed, but the fat and protein contents show an increase. This is presumably due to the concentrating effect of the decreasing milk yield.

There was no difference in milk samples taken at different stages of lactation in terms of total plate count and coliform count, however, in the late stage of lactation, the somatic cell count showed an increase.

6. Acknowledgment

This publication was supported by the project EFOP-3.6.3-VEKOP-16-2017-00008. The project was supported by the European Union and co-financed by the European Social Fund.

We are grateful to the management and staff of the dairy farm for their helpful assistance.

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Characterization of Serratia species and qualitative detection of Serratia marcescens in raw and pasteurized milk by an analytical method based on polymerase chain reaction

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Characterization of Serratia species and qualitative detection of Serratia marcescens in raw and pasteurized milk by an analytical method based on polymerase chain reaction

DOI: https://doi.org/10.52091/EVIK-2021/2-4-ENG

Submitted: July 2020 – Accepted: December 2020

Authors

1 Hungarian Dairy Research Institute Ltd, Mosonmagyaróvár
2 Széchenyi István University, Wittmann Antal Multidisciplinary Doctoral School in Plant, Animal, and Food Sciences, Mosonmagyaróvár
3 Széchenyi István University, Faculty of Agricultural and Food Sciences, Department of Food Science, Mosonmagyaróvár

Keywords

nosocomial infection, Serratia species, Serratia marcescens, pathogen, prodigiosin, pigment, polymerase chain reaction (PCR), food diagnostics

1. Summary

Serratia species are opportunistic pathogenic microorganisms primarily known as nosocomial infectious agents, which can also cause food quality problems. The appearance of the extracellular pigment-producing Serratia marcescens in cow’s milk causes its red discoloration, posing a challenge to the dairy industry and food certification laboratories. The detection of the bacterium by conventional procedures based on microbiological methods is time-consuming and labor-intensive, and in many cases does not lead to satisfactory results due to the competitive inhibitory effect of the accompanying microflora. Following the analysis of the relevant literature, the published endpoint PCR methods and the primers used for the detection of S. marcescens were evaluated in in silico and in vitro assays, and then the procedure was tested on farm milk samples. Using the method, a total of 60 raw and pasteurized milk samples were analyzed, more than half of which (i.e., 32) were identified as S. marcescens positive. The significance of our work is mainly represented by the application of the published test methods in food industry practice. Our results highlight to the importance of detecting this bacterial species.

2. Introduction and literature review

Nowadays, the impeccable quality and long shelf life of foods is a basic requirement of consumers. Accordingly, there is a growing demand for ever faster, more accurate and more reliable food diagnostic procedures. In this context, molecular diagnostic methods are gaining ground, for example in the rapid detection of pathogenic microorganisms. Polymerase chain reaction (PCR)-based diagnostic kits suitable for the identification of pathogenic microbes are produced by many manufacturers, and these are also used successfully in Hungarian food testing laboratories. These molecular biological tests are mainly suitable for the detection of microbes hose presence poses a high risk to public health (e.g., Escherichia coli, Salmonella Typhimurium, Listeria spp.). Less attention is paid to pathogens that are not required to be tested by law, such as Serratia species present in raw and pasteurized milk.

Serratia species are found in many places in our environment [1]. They are saprophytes or opportunistic pathogens [2], facultative anaerobic, biofilm-forming organisms [1, 3]. S. marcescens grows particularly well in phosphorus-containing environments (e.g., soaps, shampoos) and is also resistant to certain disinfectants [4, 5], so it can cause various nosocomial diseases [6, 7, 8]. Increasing antibiotic resistance of S. marcescens has also been reported in the literature [8, 9, 10]. The bacterium therefore survives and grows easily, so it may find its way into foods under inadequate hygienic conditions. Presumably, it can enter drinking milk as a result of violating hygiene rules, it can grow there and degrade the quality of food [1, 11, 12]. For some species, spoilage is indicated by a characteristic red hue.

In the case of the Hungarian dairy sector, accurate data are not available on the extent of the prevalence of Serratia species and S. marcescens, and on which species cause the infections and degrade milk quality. Nor is there a Hungarian survey on the extent of Serratia contamination of dairy farms. With the exception of a few publications, the available information on the exposure of the dairy industry to Serratia is also lacking at the international level. Such exceptions are a scientific article on the epidemic of mastitis caused by S. marcescens at Finnish diary farms [1], and an older report discussing the role played by pigment-forming Serratia species in mastitis [13].

The following Serratia species may be responsible for the red discoloration of milk: S. marcescens, S. rubidaea, S. plymuthica and S. nematodiphila (Table 1). According to their incidence, S. marcescens is of greater importance. Their characteristic pigment is the red prodigiosin, a water-insoluble secondary metabolite that is produced under specific environmental conditions [14, 15, 16, 17] (Figure 1). The typical red colonies appearing on the culture medium alone do not provide sufficient information to identify Serratia, as certain species of many other genera, not belonging to Enterobacteria, may also produce prodigiosin [14, 18].

Table 1. Characterization of Serratia species and their pigment production [19–22]
Figure 1. Pure culture of Serratia marcescens on tryptone-soy agar (TSA) (30 °C, 48 h)

There is currently no ISO standard for the detection of Serratia species in foods. In their 2006 book chapter [9], Grimont and Grimont discuss the characteristics of the genus Serratia, as well as aspects of their isolation and identification. However, identification by classical microbiological methods is rather cumbersome and often ineffective due to the inhibitory effect of the accompanying flora, despite the fact that the pink discoloration of the milk sample is clearly visible to the naked eye. Although culture media are available for the selective growth of the bacterium [47], in practice their use does not provide a satisfactory solution. In addition, conventional methods are time and labor intensive.

There are commercially available rapid methods for the determination of S. marcescens, for example the miniaturized test kit from bioMérieux called Rapid ID 32 E, which satisfies the requirements of standard ISO 7218 [48]. However, a colony growing on a culture medium is required to perform the test. Diagnostic tests based on the PCR method, as mentioned before, could provide a solution to overcome the difficulties of detection. At present, however, only the Genesig product of Primerdesign can be mentioned as a molecular diagnostic kit for the detection of S. marcescens [49].

The literature relevant for the food industry and, in particular, the dairy industry, is rather poor on the detection of Serratia species, including S. marcescens, by either endpoint PCR or real-time PCR methods. Hejazi et al. [50] carried out the serotyping of S. marcescens by the RAPD-PCR technique. Serological samples from patients in need of hospital care were used in their study. Iwaya et al. [6] also tested blood samples for S. marcescens strains using a real-time PCR method. Zhu et al. [51] performed molecular characterization of S. marcescens strains by RFLP and PCR methods, while Joyner et al. [2] detected S. marcescens strains in marine and other aquatic environmental samples (e.g., coral mucus, sponge pore water, sediment, sewage, wastewater and diluted wastewater) by real-time PCR. A study of Bussalleu and Althouse, published in 2018, reports a conventional endpoint PCR technique suitable for the identification of S. marcescens that effectively detects the presence of the microorganism in wild boar semen [52].

Our goal was the set up a classical PCR method suitable for the detection of S. marcescens in milk. The significance of our work lies in the fact that PCR-based methods described in the literature and the primers used were analyzed, then the procedure deemed appropriate was adopted to food hygiene analytical practice. In our experiments, qualitative determination of the possible S. marcescens contamination underlying the discoloration of factory, raw and pasteurized milk samples was performed.

3. Materials and methods

3.1. In silico studies

Based on the literature, three primer pairs (Table 2) were selected, which were evaluated by computer modeling, by so-called in silico analysis, as well as in vitro experiments in order to find the most suitable one for subsequent PCR assays.

Table 2. Serratia marcescens-specific primer pairs used in this study

In our in silico studies, the specificity of the a primer sequences was verified by comparison with a DNA database (NCBI BLAST) [54]. Comparison with the database allows for homology search (“blasting”). Following this, the suitability of the primers, i.e., whether a possible PCR reaction takes place with the selected genomes, was tested with a molecular biology software (SnapGene 5.1.5.) [55]. In the latter case, positive and negative control genomes were downloaded from the NCBI database, and then the SnapGene software was used, in an in silico way, to investigate whether the PCR reaction would take place with the primer pairs. The positive and negative controls used for reference purposes were whole chromosome genomes (Table 3).

Table 3. Genomes of bacterial strains used as positive and negative controls in in silico analyses and their reactions to primer pairs

* Primerek: A. Fpfs1 és Rpfs2; B. FluxS1 és RluxS2; C. Serratia2-for és Serratia2-rev.

Jelmagyarázat:

3.2. In vitro experimental studies

To confirm the results of the in silico studies, in vitro were performed in which the selected primer pairs were tested in laboratory PCR analyses on genomic DNA samples of selected strains of bacteria (several S. marcescens strains were used as positive control and Lactobacillus delbrueckii subsp. delbrueckii, Streptococcus thermophilus, Enterococcus faecalis and Micrococcus luteus were used as negative controls). The microorganisms were bacterial strains belonging to the collection of MTKI Kft. and coming from factory environment, determined by genetic identification.

When putting together the components required for the PCR reaction, 5.2 µL of PCR grade sterile water, 10 µL of DreamTaq Green 2× PCR Master Mix (Thermo Fisher Scientific, Waltham, Massachusetts, USA), 0.4 µL (10 pmol/µl) primer and 4 µL of isolated bacterial genomic DNA were used for each reaction. The negative control of the reactions was PCR grade sterile water. The program parameters of the PCR instrument (Mastercycler Nexus Gradient; Eppendorf International, Hamburg, Germany) were as follows: 95 °C for 1 minute, then for 40 cycles 95 °C for 15 seconds, 59.5 °C for 15 seconds, 72 °C for 10 seconds and, finally, 72 °C for 7 minutes [52].

For size separation of the DNA segments formed during the PCR reaction, a 10 µL sample was analyzed on a 2% agarose gel [TBE buffer (Tris-borate-EDTA) (10×), Thermo Fisher Scientific; Agarose DNA Pure Grade, VWR International, Debrecen, Hungary; ECO Safe Nucleic Acid Staining Solution 20.000×, Pacific Image Electronics, Torrance, California, USA]. The DNA size marker was the GeneRuler Low Range DNA Ladder (Thermo Fisher Scientific). Gel documentation was performed using the Gel Doc Universal Hood II gel documentation equipment and software (Bio-Rad, Hercules, California, USA).

3.3. analysis of raw and pasteurized milk samples

On the one hand, we used in our study factory raw and pasteurized milk samples in the case of which S. marcescens contamination was suspected due to their pink discoloration. On the other hand, factory raw and pasteurized milk samples that arrived at the laboratory together with the above samples but not exhibiting discoloration were also tested.

For the DNA digestion and purification process, the NucleoSpin Microbial DNA kit (Macherey-Nagel, Düren, Germany) was used according to the manufacturer’s instructions. The reaction tubes containing the eluted DNA were stored in a freezer at -20 °C.

Next, the suitability of DNA isolation and the amplifiability of the samples were checked by 16S rDNS polymerase chain reaction, using primers 27f (5’-AGAGTTGATCMTGGCTCAG-3’) and 1492r (5’-TACGGYTACCTTGTTACGACTT-3’). The total volume of the PCR reaction for 1 sample was 5.6 µL of PCR grade sterile water, 10 µL DreamTaq Green 2× PCR Master Mix, 0.2 µL (10 pmol/µl) of the primers and 4 µL of isolated bacterial genomic DNA. The negative control of the reactions was PCR grade sterile water. The program parameters of the PCR instrument were as follows: 95 °C for 4 minutes, then for 40 cycles 95 °C for 20 seconds, 54 °C for 30 seconds, 72 °C for 1 minute and, finally, 72 °C for 5 minutes.

For the separation of the DNA segments formed during the PCR reaction, a 5 µL sample was analyzed on a 1% agarose gel. The DNA size marker was the GeneRuler 1 kb Plus DNA Ladder (Thermo Fisher Scientific). The DNA sample tested was judged to be suitable for further PCR analysis if the length of the copies of the amplified DNA fragment was as expected (~1500 bp).

In the next step, samples were subjected to S. marcescens-specific PCR analysis and gel electrophoresis as described in subsection IN VITRO EXPERIMENTAL STUDIES. The results were evaluated on the basis of the presence/absence principle.

In order to check the suitability of the method, PCR results of the milk samples were compared with the few available API (bioMérieux, Budapest, Hungary) test results in a control test. The method was then used to detect the presence of S. marcescens in raw and pasteurized milks.

4. Results

In our in silico studies, when examining the homology of the primers, they showed similarity primarily to S. marcescens chromosome genomes. However, matches were also found in the case of S. rubidaea and S. nematodiphila strains and some non-Serratia species. These results were taken into account during the selection of reference genomes designed for our SnapGene software studies. The need for further investigation was justified by the fact that appropriate homology or the matching of the basis do not automatically mean that the PCR reaction will take place, because the direction of the primers, their melting temperature and the size of the PCR product formed are also critical, among other things.

In the SnapGene test, PCR reactions were predicted with the following parameters: our analyses were performed with at least 15 bases matching and the exclusion of single isolated mismatches. The minimum melting temperature was 50 °C and the maximum length of the fragment obtained as the result of the amplification was 3 kbp.

As shown in Table 3, when matched with the S. marcescens genomes, the primer pair Serratia2-for and Serratia2-rev showed amplification in all cases. The PCR reaction generally resulted in six or seven amplicons on the 16S rDNA sections. The adhesion site of the Fpfs1–Rpfs2 and FluxS1–RluxS2 primer pairs is located outside the 16S rDNA in most S. marcescens strains, but in some cases they did not show in silico amplification, so their sensitivity did not prove to be adequate. In the negative control genomes, the completion of a PCR reaction was predicted by the primer pair Serratia2-for and Serratia2-rev in some cases for certain S. rubidaea and S. nematodiphila strains. Using primers Fpfs1–Rpfs2, the PCR reaction would take place in the case of a S. nematodiphila strain. Primers FluxS1–RluxS2 did not predict the occurrence of a reaction on any of the selected negative control genomes (Table 3).

In S. marcescens genomes selected as positive controls in in vitro experiments, all three primer pairs gave signals according to the expected fragment size, and none gave a signal on the negative controls. The analysis carried out with the primer pair Serratia2-for and Serratia2-rev is shown in Figure 2. In the case of negative samples, the weak signals at around 50 bp are caused by the accumulation of the byproduct aspecific DNA fragments, primer dimers.

Based on the results of in silico analyses and in vitro studies, primers Serratia2-for and Serratia2-rev were considered to be suitable for further work, despite the fact that their specificity was not perfect. The decision was based on the probable frequency of occurrence of S. marcescens on the one hand and the importance of avoiding samples with false negative results on the other.

In order to check the suitability of the method that had been set up, factory milk samples were tested in a control study. Some of the milk samples (n=10) exhibited pink discoloration. Using our test method, nine samples were found to be positive for the microbe sought. We also had API test results for four of the samples. The four API-positive samples were also found to be positive in the PCR assay. The method was then used to detect S. marcescens in raw and pasteurized milks.

Some of the milk samples showed peach-pink discoloration (Figure 3), but it was not clear in many cases due to the pale or yellowish tint. A total of 60 samples were analyzed. Of these, 32 (53.3%) gave positive results and 28 (46.7%) gave negative results for the presence of S. marcescens.

Figure 4 shows the result of one of our assays, the separation by gel electrophoresis. It can be clearly seen that the positive control strain gave a positive signal, while the negative control sample gave a negative signal, and positive signals were obtained for three test samples. The weak signals appearing in the case of negative samples are again caused by the accumulation of primer dimers.

Figure 2. Results of PCR analysis with Serratia2-for and Serratia2-rev primers on the genome of selected bacterial strains. Lanes: 1. Serratia marcescens 551R; 2. Serratia marcescens 1911; 3. Lactobacillus delbrueckii subsp. delbrueckii 0801; 4. Streptococcus thermophilus 1102; 5. Enterococcus faecalis 1101; 6. Micrococcus luteus CLTB1; 7. Negative control (sterile water); M: Molecular weight marker
Figure 3. Milk samples. Left sample is netive and right sample is positive for Serratia marcescens, based on the result of PCR test
Figure 4. Gel electrophoresis image of Serratia marcescens-specific PCR assay. Lanes 1 to 7: Milk samples; K+: Positive control (genomic DNA from Serratia marcescens); K-: Negative control (sterile water); M: Molecular weight marker

5. Discussion

When evaluating our results, it is important to take into account that the PCR analysis is a method suitable for the amplification and detection of the target DNA in the sample, based on which it is not possible to determine whether the amplified S. marcescens-specific DNA comes from viable, dead or so-called VBNC cells. In the VBNC (“viable but not culturable”) state, the cells are viable, metabolically active, but cannot be propagated by classical culture methods. This condition is reversible.

The objective of our work was to establish a classical PCR method for the detection of S. marcescens. Using the test procedure applied, qualitative determination of the S. marcescens contamination responsible for the discoloration of milk samples can be carried out.

Although the experiments presented here focused on the detection of pigment-producing S. marcescens, a future genus-level study could identify all 20 Serratia species (Table 1). The significance of the detection of other Serratia species is evidenced by the fact that, although the genus Pseudomonas is the main cause of the spoilage of chilled raw milk, the dangers of Serratia species in this respect are also known [56]. In addition to Pseudomonas strains, Serratia strains have also been identified in many cases as causes of milk spoilage. Members of the genus Serratia have been detected in dairy plants [3, 12], in raw milk samples stored at 4° C [56, 57, 58] and in milk containers [59]. It was noted by Grimont and Grimont [9] already a decade and a half ago that raw milk lots can occasionally be contaminated with Serratia species, and the species most often occurring in diary products are S. liquefaciens and S. grimesii.

The presence of psychotrophic Serratia species (e.g., S. liquefaciens) in raw milk can cause spoilage even after heat treatment. Baglinière et al. found that the thermally stable Ser2 protease produced by S. liquefaciens may be a significant factor in the destabilization of UHT milk [11, 60].

In conclusion, it can be stated that a genus-level study would be an interesting research project that would fill a gap, and which would allow the monitoring of raw milk in this respect, the wide detection of Serratia species. Presumably, the results would provide useful information not only to the stakeholders of the dairy economy and the dairy industry, but could also have an impact on Hungarian regulatory and monitoring practice.

6. References

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Comparison of the mechanical fatigue indices of Golden Delicious apples and Packham pears

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Comparison of the mechanical fatigue indices of Golden Delicious apples and Packham pears

DOI: https://doi.org/10.52091/EVIK-2021/2-5-ENG

Received: August 2020 – Accepted: January 2021

Authors

1 Szent István University, Faculty of Mechanical Engineering, Institute of Process Engineering, Gödöllő (Since 01. Feruary 2021: Hungarian University of Agriculture and Life Sciences, Institute of Technology)
2 Szent István University, Faculty of Mechanical Engineering , Institute of Machinery and Informatics, Gödöllő (Since 01. Feruary 2021: Hungarian University of Agriculture and Life Sciences, Institute of Technology)

Keywords

fruit damage, TTF (time to failure), rheological testing of fruits, viscoelastic models, time-dependent deformation, loading and unloading curves, dissipated energy, biological yield point, biological rupture point, damage limit value, damage resistance, creep curve, deformation

1. Summary

One of the most significant phenomena in the processing of horticultural crops, leading to the damaging of the fruit, is fatigue due to repeated mechanical stress, which endangers the integrity of the produce, especially during transport. In the event of such damages, the immediate environment of the damaged fruit, or even the entire batch of crops may be in danger, as the biological processes leading to spoilage are not limited to the individual crop damaged. In the case of repeated effects, a force less than the static limit value is sufficient to cause spoilage, but in addition to the load, the material properties of the given crop, as well as the energy balance observed during damage play important roles in determining the mechanical resistance. Accordingly, in our work, a description of the spoilage process is built on the material models most characteristic of the selected crops, on the dissipated energy indicators measured during repeated loads, and on the definition and determination of the spoilage time. In the experiments, the fatigue indices of Golden Delicious apples, making up most of the apple production of the European Union, and of long shelflife Packham pears are compared by setting up linear regression models.

2. Introduction

When sorting produce, not only the size and shape, but also the extent of a possible damage or, in many cases, the fact of the damage itself is the basis for the selection. Automated machine recognition, which in most cases is performed by spectral imaging methods, today can effectively separate damaged crop tissues from healthy ones and finding damages under the surface which are not visible to the naked eye does not pose a problem to the technology either [1, 2]. Reliability depends on the hardware design (i.e., the accuracy of the equipment used) on the one hand, and on the algorithms used [3]. In addition to sorting, the method also uses camera monitoring, which can take into account the ripeness of tomatoes with the help of the appropriate software, and which allows the fully automated operation of the harvesting robots [4].

Although with effective detection the damaged crops can be easily removed from the processing chain, in addition to screening, the objective of getting as many healthy goods as possible to customers after the harvest, and the necessary treatment processes must also be kept in mind. Since international surveys show that a significant proportion of crops does not reach consumers in the market due to losses at different stages of processing [5, 6, 7], in addition to the precise detection of injuries, prevention must also play a key role. This also requires destructive testing of the crops and the direct observation of spoilage processes.

The material properties of various agricultural and horticultural crops can be described using viscoelastic models, consisting partly of elastic and partly of viscous components. Complex material structures can also be built from basic elements connected in a serial or parallel way, and of the three-element systems, the Poynting-Thomson model has been used several times in previous research to characterize Maloideae [8, 9, 10]. In the case of viscoelastic systems, deformation due to mechanical interactions depends not only on the magnitude of the stress, but also on the speed of the load, and creep and relaxation are an important part of the load and deformation process: while in the case of the former, a constant load results in increasing deformation, in the case of the latter phenomenon, a constant deformation results in a continuous decrease in stress [11].

The reaction of a given crop to a mechanical impact is shown on the load-deformation curve which, in addition to the creep and relaxation parameters, provides information on the total amount of energy generated in the load process: the area enclosed by the load and unload curves also serves as the basis for dissipated energy calculations in other fields [12, 13], and it is closely related to the viscoelastic properties of the test material and, in the case of crops, to the mechanical resistance and the susceptibility to damages [14].

The load limit that leads to microscopic damage to the cell structure, which can also cause crop spoilage, is called the biological yield point. Although as biological materials, different crops may be capable of healing or even complete regeneration, mechanical impacts applied during processing should be kept below the biological yield point. The limit value can also be indicated by a damage visible to the naked eye and affecting a larger area, which is called the rupture point in the literature. In the case of such damages, the crop is very likely to spoil [15, 16]. There is usually a significant variance between damage limits (even in the case of the same exact load), which is also affected by the ripeness of the given crop, as well as the conditions provided during storage and processing.

In additions to collisions resulting from improper handling, most damages are caused by vibrations during transport. Unfortunately, the observation of processes that end in damages by destructive tests is not an area that today’s research focuses on, although the mapping of fatigue due to repeated loads is also essential in fruits [17].

During transport simulations, the frequencies causing the greatest damage have already been unanimously identified [18, 19, 20], so in the case of destructive tests with repeated loads, experience shows that it is advisable to set the frequency range below 10 Hz.

Multivariate regression models that take into account different test parameters are often used to describe the mechanical properties of fruits and vegetables [21, 22]. The objective of our research was to study the less discussed phenomenon of fatigue in crops, and to determine the relationship between damage limit values (biological yield point or rupture point) and related factors (energy balance, material properties). The goal was to establish a linear equation for the damage limit value, which is determined by considering the parameters that can be measured during repetitive compressive load.

3. Materials and methods

3.1. Measuring instrument and the securing of the fruits

Destructive tests were performed with the instrument called DyMaTest, provided by the Hungarian Institute of Agricultural Engineering of NAIK. The instrument applies a load to the fruit with a cylindrical (flat-faced) pressure pin, and the pressure force can be adjusted arbitrarily using the software interface developed for the instrument [23]. The deformation of the crop can be registered with a laser sensor that detects the movement of the measuring pin, and the force can be registered with a special measuring cell designed for the instrument. Tests were performed after setting a sinusoidal pressure force up to the fruit failure limit.

To perform the measurements, the crops were secured in a sand bed. To check that the creep of the sand applied did not affect the results obtained, control measurements were performed using a completely inelastic bearing ball with a diameter of 32 mm. During the compressive loads, there was no detectable displacement in the measuring range of the photoelectric sensor, so the deformation of the sand does not appear on the load curves of the fruits at all. Prior to testing, sand preparation consisted of wetting, sieving and compaction operations [24].

3.2. Crop deformation curves

For the tests with repetitive loads, a cyclic waveform was used, which can be characterized by the following function:

Fm = Fmax(1-cos(ωt))

where Fmax is the peak value of the periodic load function [N] and ω is the angular velocity of the load [s-1].

The resulting deformation due to periodic loading is also periodic. Figure 1.a shows the time function of the deformation of a Golden apple, while Figure 3.b shows the force-deformation curve. Typical deformation curves for Packham pears are shown in Figures 3.c and 3.d.

As a result of the cyclic load with a constant amplitude, the deformation changes continuously, and this can be noticed in the increase of the envelope (or the mean). Since these envelopes increase similarly to the creep curves observed under static loading, this process is called dynamic creep [25].

The response function to the cyclic load can be described by the following equation:

Wm = β+Wmax(1-cos(ωt-δ))

where w is the deformation [mm], β is the creep term, wmax is the peak value of the periodic deformation function [mm], ω is the angular velocity [s-1], and δ is the phase shift between the load and deformation time functions.

To characterize creep (in this case, to give ), the literature generally uses a linear approximation. Although this approximation may be appropriate for a significant region of the creep in most cases, the initial and failure sections of the curve cannot be linearized, so the method carries inaccuracies when considering the entire creep process. In order to avoid this, numerical solutions were used in the data management processes related to deformation, in which the operations were performed not by approximation, but by direct processing of the data series.

In the case of the curves shown in Figure 1, the damage limit of the fruits, in this case the rupture point, has already been determined, and the data after this point have been removed from the diagrams. By analyzing the curves obtained this way, we can actually obtain information about the energy conditions taking place until failure, as well as about the material properties experienced this far.

Since the rupture point cannot be distinguished clearly during the analysis of the diagrams in many cases, especially in the case of loads that take place rapidly and the concomitant sharply changing deformation processes, accurate determination was therefore performed by high frame rate video surveillance (Figure 2). The camera used recorded 240 frames per second, and the rupture point sought was the first frame of the failure phase, when the pressure pin visibly exits the slowly increasing deformation range during the creep phase and causes damage to the crop tissue that is visible from the outside by breaking the skin. In this case, both the skin and the flesh are damaged, so the material behavior is approximated by the modeling of not a structure with a homogeneous composition, but of a „structure”.

Figure 1. Time vs. deformation (a) and force vs. deformation (b) functions of a Golden Delicious apple, and time vs. deformation (c) and force vs. deformation (d) functions of a Packham pear
Deformation – Time – Force
Figure 2. Determination of the rupture point by analyzing high frame rate recording
Force - Deformation

The sampling frequency of the DyMaTest is 2 kHz, which is 8.3 times higher than that of the video recordings of the rupture point. The absolute error of the frame analysis compared to the data collected by the material testing instrument is 4.16 milliseconds, which is the lowest resolution unit of the camera. Figure 2.b illustrates the error range for the rupture point. The rupture point as a test parameter is hereinafter denoted by the notation , which refers to the term time to failure.

3.3 Viscoelastic material properties

To determine the material properties of fruits, the three-element Poynting-Thomson model was used, which had already been used in previous research projects on apples. The coupling of the model is shown in Figure 3, and it can be characterized by the following equation:

where E1 and E2 are the elastic components of the mechanical model [N mm-1] and ƞ is the viscous element [Ns mm-1]. Fm is the compressive force recorded during the measurements [N] and wm is the deformation obtained during the measurements [mm].

Figure 3. Identification of the computer mathematical model DyMaTest material testing equipment - Investigated crop - Mathematical model – Measured compressive load – Measured deformation – Calculated deformation

The block-oriented writing of the equation was performed in a Matlab Simulink environment, where the model was identified with the force and deformation data obtained during the measurements (Figure 3). The values of the elastic and viscous coefficients were determined for the calculated curve (w) that best fit the measured results ()wm). To minimize the difference between the two data sets, we used a procedure based on the least square method:

After running the minimum search process, the model coefficients E1, E2 and ƞ were recorded and were used as test parameters. The approximations carried out with the presented mathematical system provided R2 values between 0.967 and 0.998.

3.4. Analysis of the hysteresis curves

The force vs. deformation diagrams in Figures 1.b, 1.d and 4 show recurrent hysteresis processes where the area enclosed by the load and unload curves is closely related to the energy indices of the crop for the given cycle. The horizontal axis shows that the curve does not close after unloading, so a wM permanent deformation occurs in the material in each cycle until the next compressive load, and the wR elastic deformation of the given crop is due to the difference between the load peak and the permanent deformation (the sum of the two gives the total magnitude of the deformation in the given cycle).

Figure 4. Force vs. deformation curve of a single load cycle (a) and the force vs. deformation curve until failure of a crop (b) for a Golden Delicious apple
Load – Unloading - Deformation

If we examine the areas between the curves, by subtracting the energy associated with the elastic deformation (ER) from the total work (E), the dissipated energy of the cycle (ED) is obtained. This energy loss can be calculated by determining the area between the curves:

where twM is the time elapsed between the start of the loading process and the end of the unloading [s] and F is the load function produced by the test equipment [N].

Since the area calculation was performed by the numerical integration of the force and deformation data over time, the previously mentioned approximation functions and their inaccuracies associated with them can be avoided.

Although the calculation of energy losses is included in several studies that describe the damage mechanism, only a portion of the dissipated energy that can be determined from the hysteresis curve is related to material damage and the failure process [13]. In other fields, such as the rheological description of pavement asphalt layers, calculation methods have also been developed that point directly to the moment of failure using the dissipated energy data. These include the so-called dissipated energy quotient, which can be calculated by the following equation [26]:

where EDi is the total energy loss up to the given cycle [N mm] and EDn is the energy loss of the given cycle [N mm].

When the dissipated energy quotient is plotted as a function of the number of cycles (Figure 5), it can hint at two damage indicators: the onset of the cracking process of the given asphalt is indicated by a 10% drop in the ramp-up slope of the curve, and the fracture seen at the peak is the fatigue failure [26].

In the course of our experiments on fruits, the said drop in the slope cannot be observed so clearly in most cases, which is probably a consequence of the rapid load settings. However, the internal rupture point clearly appears in our own results as well. In addition to the time elapsed until the rupture point and the viscoelastic model coefficients, this data is also used to construct the equations describing the damage process.

Figure 5. Internal rupture point indicating fatigue based on the quotient calculated from dissipated energies for a Golden Delicious (a) and a Packham (b) produce Ratio of dissipated energy – Internal breaking point – Number of load cycles

3.5. Test parameters, load settings

Our objective was to describe, using parameters related to the damage process, the time to failure (TTF), which will be a dependent variable of the resulting equations. When characterizing failure, we aim to establish linear regression equations.

Compressive loads were applied to 25 Golden Delicious apples and 25 Packham pears (i.e., the number of replicates for each crop was 25), and six different measurement frequencies were used for each fruit. These frequencies fall into the range considered to be the most dangerous in transportation research, mainly in the range below 10 Hz, and taking into account the setting options of our instrument, they were 2.5, 3.7, 5, 7.5, 10 and 11.6 Hz. Thus, a total of 300 compressive loads were applied, and from the force, deformation and time data obtained during the loads, the E1, E1 and η coefficients of the material model were determined in each case, as well as the TTF time to failure and the EDRmax internal damage index, using the methods detailed above. In addition, it is also taken into account whether the process was influenced by the test frequencies.

Because of the different load resistance of the Golden and Packham crops, different compressive forces had to be applied: in the case of Packham pears, failure was already reached in one of the first cycles at certain values of the frequency range, while Golden apples were much more resistant, so considering the compatibility of the damage times and dissipated energy values to be detailed later, a load of 4 N to pears and a load of 14 N was applied to apples. In practice this means that at settings greater than 4 N, for most of the frequency values investigated, immediate destruction occurred in pears, and in the case of settings below 14 N, load processes orders of magnitude longer would have to be run to visibly damage the apples.

Results

4.1. Times to failure and energy indicators hinting at internal damage

Average and standard deviation values of the ties to failure for each frequency setting are shown in Table 1. Figure 6.a shows a chart of the average values of Golden apples, while for Packham pears, the results are shown in Figure 6.b. In the case of apples, the rupture points occurred as expected, i.e., irreversible damage occurred earlier at higher frequencies, while there was a deviation from this in the average values obtained for pears, as at settings above 5 Hz, there is an increasing trend can be seen in time to failure.

Table 1. Average times to failure and standard deviations of the results

In the case of the Golden apples, larger standard deviation values can be found at lower frequency settings, while at higher frequencies, the extreme values of the error ranges move closer to the average values. The endpoints of the standard deviation range show a similar trend to the frequency dependence of the average in Golden apples, but in the case of pears, the minimum values of the standard deviation range no longer represent the change in the average values, thus different characteristics are observed for pears between the 25 measurements.

Figure 6. Frequency dependence of the times to failure for Golden Delicious apples (a) and for Packham pears (b) Failure time - Frequency

Since the standard deviation is quite significant for both apples and pears (Table 1), the role of the additional parameters considered in the study (viscoelastic model coefficients, as well as energy indices) is particularly important when considering their effect on the damage process during the description of time to failure.

Figure 7 shows the peak values of the energy loss quotient calculated from the dissipated energy, and the results for 25 crops each were also averaged for each frequency setting.

In the case of the Golden apples examined, both the energy loss values recorded for each cycle and the maximum quotient values indicating internal rupture show a decreasing trend towards higher frequency settings, however, in the case of pears, this process is reversed, and the trend describing the frequency dependence also has a different nature.

Figure 7. Frequency dependence of the average values of accumulated dissipated energy
Maximum of dissipated energy ratio

4.2. Evaluation of viscoelastic model parameters

The frequency dependence of the elastic (E1' E2) and viscous (ƞ) material properties of the crops is shown in Figure 8, where the values of each series of measurements are displayed averaged at each frequency setting. Numerical results are summarized in Tables 2 and 3.

Table 2. Average values and standard deviations of viscoelastic model parameters at each load frequency for Golden Delicious apples
Table 3. Average values and standard deviations of viscoelastic model parameters at each load frequency for Packham pears
Figure 8. Averages of elastic and viscous model parameters for Golden apples (a, c) and Packham pears (b, d)
Viscous element - Frequency

The elastic coefficients in the case of Golden apples do not exhibit an apparent frequency dependence, while a slight decrease can be detected in the case of the E1 parameters when higher test frequencies are used. In previous experiments, apples tended to behave more rigidly at higher load velocities [25]: if a higher load velocity corresponds to a higher frequency in the present case, then this reaction is consistent with the earlier experience.

However, in the case of pears, there is a clear increase when component E1 is examined, and this result may explain the obtained time to failure data: at the frequencies above 5 Hz, a more elastic, softer surface is formed near the load zone in the pears examined, and the increased elasticity provides a more favorable mechanical resistance for the crops. Thus, in the most dangerous frequency range, higher values do not necessarily carry the most significant damage potential. The E2 elastic coefficient is constant in the studied range for both Golden apples and Packham pears.

By plotting the viscous parameters, a clear frequency dependence is obtained for both Golden apples and Packham pears. The curve obtained for apples shows a similarity to the frequency dependence of a dynamic viscosity factor presented in a previous research [27], while in the case of pears, also the frequency around 5 Hz breaks the downward trend, this may also be related to the rupture point in the frequency curve of the times to failure.

The error ranges showing the standard deviations are wider in the case of pears, the widest range of standard deviation was recorded at the 2.5 Hz setting. One of the reasons for this is that with this setting, several pears were already destroyed in the first loading phase of the first cycle.

Table 4. Analysis of variance of viscoelastic model parameters

The degree of frequency dependence was checked by analysis of variance (ANOVA) and the results are summarized in Table 4. In the case of Golden apples, a significant correlation can only be detected for coefficient η (p<<0.05), and this confirms the conclusion that can be drawn from the diagrams, which were reached in the case of coefficients E1 and E2: the elastic elements and the frequency in the studied range are not detectably related. In the case of Packham pears, however, in addition to η, the frequency dependence of the elastic coefficient E1 can also be detected, which plays a significant role in the mechanical resistance experienced above 5 Hz.

4.3. Lineáris tönkremeneteli modellek

Using the results of the tests presented and the values of the load frequencies, the possibility of four different failure modes for Golden Delicious apples is suggested, according to the following search function:

TTF = A+Bη+CEDRmax+Df+KE1+JE2'

where A, B, C, D, K are E constants. The different versions are described in Table 5. These include the elastic and viscous material properties of the crops, as well as the peak value of the dissipated energy, but not the frequency settings.

Table 5. Linear models that can be created from the measured parameters for Golden apples

(a) variable: η
(b) variables: η, EDRmax
(c) variables: η, EDRmax, E1
(d) variables: η, EDRmax, E1, E2

In the curves showing the model parameters and as the result of the analysis of variance, there was no significant relationship between the elastic coefficients and the frequency, but the elasticity for the Golden apples had a clear effect on the failure process, resulting in a detectable increase. While the elastic coefficient E1 is a defining part of the equation, E2 contributes only negibly to the accuracy of the fit, so we chose the third equation for the simplest description of the failure of Golden apples:

TFF = 0,533+2,736η+0,141EDRmax-0,261E1

The models applicable to Packham pears are summarized in Table 6. In these versions, the load frequency appears as well, playing an important role in the description of the time to failure.

Table 6. Linear models that can be created from the measured parameters for Packham pears

(a) variable: EDRmax
(b) variables: EDRmax, η
(c) variables: EDRmax, η, f
(d) variables: EDRmax, η, f, E2

Although the parameter E1 was related to the frequency, failure is not affected by this coefficient, but E2 connected in parallel with the viscous component. Since both the frequency and the elastic factor E2 contribute significantly to the accuracy of the linear approximation, a fourth equation was written for Packham pears:

TTD = 0-091+0,788η+0,085EDRmax-0,103f+1,524E2.

The results of the analysis of variance checking the validity of the equations are shown in Table 7. Since the F values obtained are considered to be significant (p<0.05), the approximations described are valid.

Table 7. Analysis of variance of the approximation equations

Time to failure results (TTFsz) of the models generated after substitution, as well as their relationships to the measured results (TTFm) are shown in Figure 9 over the entire study range. Averaged results by frequency of the approximation applied to Golden Delicious apples were between 1.54% and 3.85% relative error, while the results averaged by crop were between 1.01% and 31.13%. For Packham pears, averaging the results obtained for each frequency setting, the relative errors were between 2.42% and 6.22%, while the deviations of the values calculated for individual crops were between 0.04% and 34.51% compared to the measured time to failure. The higher error values were not related to the given frequency settings, but to the different mechanical resistance and material properties of each crop.

Figure 9. Relationship between measured and calculated times to failure for Golden Delicious apples (a) and Packham pears (b), evaluating all measurement results
Faliure time (measured) - Faliure time (calculated)

5. Conclusions

Repetitive loading during fruit processing and transport procedures causes significant damage, so in our work we investigated failure caused by fatigue, and to this end we developed multivariate linear regression models based on the most important material properties and energy indices related to the failure process, and which can predict the damage resistance of the tested Maloideae (Golden Delicious apples and Packham pears).

In some cases, the rupture point indicating failure cannot be evaluated from the deformation data obtained during the measurements, in which case limit values determined by rapid filming and frame analysis may be helpful during the analyses. The accuracy of this depends on the frame refresh rate of the cameras used, and this, together with image resolution, is constantly evolving in mobile devices, so these devices are also becoming suitable for similar measurement tasks, and their use in the monitoring of the deformation of fruits is no longer unprecedented.

Observing internal damage on the basis of energy calculations may represent a new research direction in the study of fruit damages, as environmental impacts in processing procedures need to be addressed accordingly (limitation or modification of handling, dropping and vibration limit values). However, a precise definition of the phenomenon in order to describe the damage process in the cellular structure in more detail is still awaiting microlevel investigation and confirmation.

6. Acknowledgment

The authors would like to thank the Institute of Agricultural Mechanization of NAIK for providing the DyMaTest material testing instrument. We would also like to thank Dr. László Földi for his help in computer modeling and Dr. László Székely for his help in establishing the multivariate equations.

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