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International standardization of an LC-MS/MS based food analytical method: development of a generally accepted test procedure for Alternaria toxins

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International standardization of an LC-MS/MS based food analytical method: development of a generally accepted test procedure for Alternaria toxins

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

Received: August 2021 – Accepted: February 2022

Author

1 Mertcontrol Kft.

Keywords

mycotoxin, isotope dilution mass spectrometry (LC-IDMS), standardization, citrinin, validation, HorRat value, Horwitz-Thompson equation

1. Summary

There are more than seventy varieties of Alternaria toxins, but researchers have so far identified only a few of them structurally. The objective of this paper is to present a nearly ten-year process, during which an international standard for the simultaneous analysis of five Alternaria toxins in food samples was developed. This long process includes the development of the need for the standard and, in addition to the preparation and evaluation of the standardization tender, the development of the method, its validation and documentation. The paper focuses mainly on the development and validation of the analytical method, which is the longest and most labor-intensive part of the process, but in order to understand the overall picture, it is also necessary to emphasize the first and final steps. The development of a standard is a task of great responsibility for both the preparers of the standard and those involved in the validation and documentation of the standard, as the use of standardized methods is expected by the customers of the laboratories. On the other hand, laboratories that choose unique, self-developed methods can ascertain the accuracy and precision of their procedure by comparing them with the standard method. In this process that went on for nearly ten years, the original analytical method underwent several changes; the goal of these improvements was to make the procedure as simple and reproducible as possible. This is how the use isotope dilution mass spectrometry was reached through derivatization. It is important to emphasize that one of the goals of standardization is to have an appropriate analytical method available to authority laboratories for the testing of legally prescribed food contaminants, which procedure is available to any laboratory, however, it is questionable, whether the cost of the test covers its application. Consequently, it is not necessarily the most cost-effective analysis which is recommended by the standard, which may be the cause of conflict between the professional and economic managers of a laboratory in the case of private laboratories. The final form of the liquid chromatography/isotope dilution mass spectrometry (LC-IDMS) standard method developed for Alternaria toxins is likely to be approved and published by the European Committee for Standardization (CEN) in the end of 2021 (the standard has been issued since the article was submitted: CEN EN 17521:2021 Foodstuffs - Determination of Alternaria toxins in tomato, wheat and sunflower seeds by SPE clean-up and HPLC-MS/MS. The Editor). The standard will contain the determination of tenuazonic acid (TEA), altenuane (ALT), alternariol (AOH), tentoxin (TEN) and alternariol monomethyl ether (AME).

2. Introduction

Legislation on natural (such as plant toxins) or artificial (such as residual substances) contaminants in foods is strictest in the European Union (EU) worldwide, regulating maximum allowable levels and limit values for contaminants in foods and feeds of plant and animal origin. Commission Regulation (EC) 1881/2006 [1] contains the so-called mycotoxin limit values in foods from byproducts of the secondary metabolism of molds in agricultural crops. The regulation is expanded constantly: while initially it only contained „classical” toxins such as deoxynivalenol (DON), aflatoxins (B1, G1, B2, G2, M1), fumonisins (B1 and B2) or patulin, by 2013 T-2, HT-2, and by 2016 citrinin were also included in the toxin regulation. The range of components is expanded constantly; the process is preceded by a scientific opinion formulated by the European Food Safety Authority (EFSA), as well as other impact studies. They take into account both the economic points of view of producers and the short- and long-term health risks of the toxins. Alternaria toxins are not yet regulated, the permissible limit values are expected in the 1-10 µg/kg range for ALT, AOH and AME, and in the 10-1,000 µg/kg range for TEA and TEN. The foodstuffs concerned are cereals (primarily wheat), tomato-based foods (tomato juice or puree) and products made from sunflower seeds and similar raw materials [2].

The EFSA report on Alternaria toxins titled „Scientific Opinion on the risks for animal and public health related to the presence of Alternaria toxins in feed and food” was published in 2011 [2], and it discusses their presence in various foods, human and animal health studies and their potential risks over 97 pages. A further goal of the report is to draw attention to future regulations and to the development of a uniform analytical method. Accordingly, the analysis of Alternaria toxins in wheat, tomato and sunflower seeds by liquid chromatography tandem mass spectrometry (LC-MS/MS) was published as a standardization procedure in the mycotoxin standardization tender announced by CEN in spring 2013.

3. Initial (intra-laboratory) analytical method

According to the basic requirement of the tender, the aspirant laboratory must have a valid accredited status according to standard ISO 17043, which applies to the organization of proficiency tests and means a well-defined test protocol that meets the analytical performance characteristics for single laboratory validation [3]. Lacking this, the laboratory must have a procedure previously certified by inter-laboratory validation. Due to its cost implications, the latter is a rarer case, but it is much more efficient in demonstrating the true reproducibility of the method than the requirements of standard ISO 17043, whereas the former validation only shows the in-laboratory reproducibility (intermediate precision) of the analysis.

The European Commission Joint Research Centre (JRC, Geel, Belgium) is a joint research center within the EU, which until 2017 included the EU Reference Laboratory for Mycotoxins (EU-RL for Mycotoxins). In 2013, an LC-MS/MS method was developed as an EU-RL method for wheat, tomato juice and sunflower seeds for the following five main Alternaria toxins (Figure 1): tenuazonic acid (TEA), altenuane (ALT), alternariol (AOH), tentoxin (TEN) and alternariol monomethyl ether (AME) [4]. Of the five toxins, TEA has the most different structure and physicochemical properties (chelating properties) from the other toxins [5]. Accordingly, previous literature has focused on the determination of TEA [5], or the other toxins [6], less attention has been paid to their simultaneous analysis. Our goal was a five-component simultaneous analysis, which was achieved by chemical derivatization. The structure of TEA contains an aldehyde functional group that is highly reactive with 2,4-dinitrophenylhydrazine (DNPH), and the physicochemical properties (e.g., Log P, octanol-water distribution) of the resulting TEA hydrazone are much closer to those of the other Alternaria toxins from a chromatography point of view [5]. In the derivatized form, it loses its chelating properties. DNPH reacts only with TEA among the target components (Figure 2), it does not interfere with the determination of the others [7]. The extraction procedure arrived at in the method was developed using an experimental design with a sample of rye naturally contaminated with the toxins. In addition to Alternaria toxins, citrinin was also included in the method. The main characteristics of the method developed in this way are the following [4], [7]:

  • Analysis of six components (TEA, ALT, AOH, TEN, AME and citrinin);
  • Matrices: cereals, tomato juice, peeled sunflower seeds;
  • Sample weight for liquid samples: 1.0 g;
  • Extraction solvent for liquid samples: 5 mL of methanol;
  • Sample weight for solid samples: 2.0 g;
  • Extraction solvent for solid samples:15 mL of methanol-water (70/30, v/v) mixture;
  • Derivatizing agent: 0.58% DNPH in aqueous hydrochloric acid;
  • Stop reagent: 5% (v/v) undecanal in methanol;
  • Sample purification: polymer-based solid phase extraction (SPE);
  • Sample evaporation and redissolution in methanol;
Figure 1. The structure of Alternaria toxins and their most important property
Figure 2. LC-MS/MS chromatograms of Alternaria toxins and the citrinin (10 µg/kg); (the TEA was in derivatisation form)
  • Syringe filtration on PTFE filter;
  • LC-MS/MS separation: acidic eluent, C-18 stationary phase and ESI negative ionization (Table 1);
  • Syringe filtration on hydrophilic PTFE filter;
  • Calibration: matrix-matched calibration without isotope-labelled internal standard.
Table 1. Ion transitions of Alternatria toxins and citrinin using ESI negative ionisation and chemical derivatisation
Table 2. Results of Alternaria toxins in proficiency test. The samples (tomato juice) and the standard solution were also tested after derivatization

In addition to the in-laboratory validation of the method, we also participated in an international proficiency test organized by the Bundesinstitut für Risikobewertung (BfR, Berlin, Germany) as a National Reference Laboratory (NRL) for the determination of the five Alternaria toxins in tomato juice. During the analysis, the five toxins had to be determined in three samples and a standard solution [7]. Our results are shown in Table 2. All reported values were acceptable, with Z-score values between -2 and +2. The results showed that the method recommended by us in the tender is suitable for the standardization of Alternaria toxins.

4. Modified analytical method

The analytical method proposed by the JRC was adopted by CEN and the mandate (mandate M/520) was given to the JRC in 2014. However, the working group (TC 275 WG 5 „Horizontal Methods for Food – Biotoxins”) did not support chemical derivatization in the method on the grounds that it is an additional and time-consuming step in the method, which may reduce its precision and should be avoided. The determination of citrinin could not be included in the method either, the analysis could only contain Alternaria toxins.

TEA can also be analyzed in its native form, but in this case HPLC separation has to be carried out with an alkaline eluent, requiring a stationary phase that is stable up to pH 9. The method has indeed become simpler without derivatization (Figure 3), but this has required significant modifications to maintain the accuracy of the procedure. In addition to being time-consuming, another disadvantage of derivatization was an increase in the noise level, as many matrix-forming compounds also react with DNPH, which can co-elute with the target components, increasing the noise in the MS/MS instrument. In the modified method, essentially the HPLC separation had to be optimized and an extraction medium had to be selected which ensured the best possible recovery from each matrix.

The main characteristics of the method developed in this way [8]:

  • Analysis of five components (TEA, ALT, AOH, TEN and AME);
  • Matrices: cereals, tomato juice, sunflower seeds;
  • Sample weight for liquid samples: 2.0 g;
  • Extraction solvent: 15 mL methanol/water/acetic acid (80/19/1, v/v/v);
  • Sample purification: polymer-based solid phase extraction (SPE);
  • Sample evaporation and redissolution in 100 µL of dimethyl sulfoxide and dilution with 900 µL of water;
  • Syringe filtration on hydrophilic PTFE filter;
  • LC-MS/MS separation: eluent with alkaline pH (pH 8.7), C-18 stationary phase and ESI negative ionization (Table 3);
  • Calibration: matrix-matched calibration without isotope-labelled internal standard.

This modified method was accepted by the working group and, following its in-laboratory validation, the inter-laboratory validation of the analytical method could also begin in spring 2015.

Figure 3. LC-MS/MS chromatograms of Alternaria toxins (10 µg/kg) using basic pH eluent without derivatisation
Table 3. Ion transitions of Alternatria toxins and citrinin using ESI negative ionisation without chemical derivatisation

5. Inter-laboratory validation of the method

The most important part of the standardization process is the inter-laboratory validation of the method, the main purpose of which is to check and evaluate the reproducibility of the analysis. To do this, the concentrations of the toxins must be determined in naturally contaminated (at low, medium and high levels) and spiked samples. To evaluate the concentration of a given component n a given sample, a minimum of eight independent values are required, however, only the results of two laboratories can be excluded [9]. It is advisable to involve at least fifteen laboratories in order to have an adequate number of results for each sample and component. This is so because, based on experience, about 2-3 laboratories do not report results, while some samples and their components do not always produce a sufficient number of reported results. This can occur mainly at low concentration levels because not all participants possess instruments with adequate sensitivity.

If, during the validation, the goal is to determine components that have long been analyzed (such as DON or aflatoxins), it is relatively easy to ask laboratories with the necessary experience for validation, based on their successful participation in previous proficiency tests. However, Alternaria toxins are still analyzed by very few laboratories to this day, so laboratories applying for the validation do not always have prior experience. For this reason, organization of a so-called pre-trial becomes necessary, in which the laboratories participating in the validation can master the method in advance. In this case, the pre-trial was performed with twenty-five laboratories, analyzing tomato juice samples [8], and only three of the participating laboratories had prior knowledge of Alternaria LC-MS/MS analysis. Of the twenty-five laboratories, only sixteen eventually participated in the final validation, because either they did not return any result or their results differed significantly from the consensus average.

During the final validation, the following samples were sent to the sixteen laboratories [8]:

  • Cereals naturally contaminated with Alternaria toxins: wheat, triticale and sorghum;
  • Tomato juice naturally contaminated with Alternaria toxins: 3 batches;
  • Sunflower seeds naturally contaminated with Alternaria toxins: 2 batches of unpeeled seeds and 1 seed mixture, which was a mixture of peeled and unpeeled seeds;
  • Participants received each sample under two codes (blind replicates) so that we could evaluate repeatability within the laboratory and to have more date available to analyze reproducibility;
  • For the preparation of spiked samples, separate test samples were sent for each matrix, for which a standard solution mixture containing Alternaria toxins in unknown concentrations was also provided to the participating laboratories. Spiked samples were prepared by the laboratories according to the „spiking guide”;
  • Blank samples for each matrix for matrix-matched calibration;
  • In the case of sunflower seeds, the blank was peeled sunflower, because the unpeeled samples are high in TEA and therefore not suitable for calibration;
  • The analytical standards of the target components and their standard solution mixture were also provided, so that all laboratories would use the same calibration solution, and no deviation would result from this;
  • The homogeneity of the samples was checked according to the harmonized protocol before dispatch [10];
  • Simultaneously with the sending of the samples, stability testing of the samples was initiated at different temperatures and for different durations.

Concentration levels required by CEN for validation: 1-10 µg/kg for ALT, AOH and AME, and 10-1,000 µg/kg for TEA and TEN. Recovery was assessed from the concentrations measured in the spiked samples, with spiking levels of 2 and 8 µg/kg for ALT, AOH and AME, and 50 and 200 µg/kg for TEA and TEN. These levels were unknown to participants.

Statistical evaluation of the results obtained (concentrations not corrected for recovery) focused mainly on reproducibility [9]. The reproducibility of the method is well characterized by the so-called HorRat value. The HorRat value is the quotient of the reproducibility of a given target component calculated for the given sample and the target reproducibility expected by the organizers. The latter reproducibility value (the „target reproducibility”) can be calculated from the Horwitz-Thompson equation: below 120 µg/kg it is uniformly 22%, while above this value the classical Horwitz relationship can be applied [11]. Based on the validation criteria, the HorRat value must be less than two; this condition was indeed met, except for TEA, in the case of the unpeeled sunflower samples. Table 4 shows the HorRat values calculated for TEA in the case of different sunflower samples. While in unpeeled sunflowers the calculated HorRat values were uniformly 2.4 regardless of the concentration [8], in the case of peeled samples, which contained much lower concentrations of TEA, the values were below two. The lower reproducibility observed during the analysis of unpeeled samples can be explained by the calibration and the matrix effect, which is a typical feature of LC-MS/MS-based measurements, and mainly effects the precision and accuracy of the method [11]. During the validation, a peeled sunflower sample was provided for calibration, because it contained a small amount of TEA contamination of natural origin, as opposed to unpeeled sunflower that was contaminated with high concentrations of TEA. The extracts of the unpeeled and peeled sunflower samples contain significantly different matrices, which can even be noticed by their color. Consequently, the calibration from the peeled sample could not compensate for the matrix effect in the unpeeled sunflower samples, so the detected concentrations were significantly affected by the matrix effect. The reason for this is that the endogenous constituents of unpeeled sunflower differ from those of peeled sunflower.

It is important to note that laboratories reported only the detected concentrations; the measured values were not corrected for recovery, in contrast to the usual procedure for conventional proficiency tests. Different laboratories used different instruments in which the matrix effect during the analysis of unpeeled sunflowers may have been different. Since the calibration recorded from the peeled sample did not adequately compensate for the matrix effect, there were large differences between the values measured by the participants. The same problem did not occur in the analysis of peeled sunflowers, because a similar degree of matrix effect may have occurred in the calibration and the test sample, due to the similarity of the samples. It is worth noting that the repeatability was also acceptable in the case of unpeeled samples (<20%). The reason for this is that repeated analysis of the same sample has the same matrix effect in the same instrument, so laboratories detected similar concentrations within the laboratory for duplicate samples, while inter-laboratory results were different due to the different matrix effects in the different instruments.

Table 4. HorRat values calculated for TEA for sunflower samples with matrix-matched calibration.

6. Final method with isotope dilution and its inter-laboratory validation

As the HorRat values were not below two for all components and samples during the validation, further development of the method became necessary. The reproducibility of LC-MS/MS methods can be greatly enhanced by isotope dilution (Isotope Dilution Mass Spectroscopy – IDMS), which compensates well for the matrix effect varying from sample to sample. In this case, a stable isotope-labeled analogue of the target compound is added to the sample as an internal standard (ISTD). the physicochemical properties of the internal standard are the same as those of the target component (a small difference in polarity may occur with deuterated standards), so the target compound and its isotopically labeled analogue ideally elute at the same retention time. As a result of the co-elution, the target component and its internal standard are subjected to the same direction and extent of matrix effect in the ion source, so the ratio of the responses (areas) of the target compound and the ISTD, the isotope ratio (IR), will be independent of the matrix effect.

The ISTD does not interfere with the signal of the target component, because it is detected at other m/z values that are sufficiently distant (preferably at least +3 Da) from the m/z value of the target component due to the isotope label.

This requires isotope-labeled ISTDs, which were not yet available in 2015, so we first used matrix-matched calibration. However, stable isotope-labeled ISTDs (labeled with 13C or deuterium) of Alternaria became commercially available in 2018 (TEA-13C2, ALT-d6, AOH-d3, TEN-d3 and AME-d3), making revalidation of the method possible using the IDMS technique.

After 2018, the JRC repeated the in-laboratory and inter-laboratory validation using the method supplemented with isotope-labeled ISTDs (Table 5). The concept was the same during the first and second validation, with the difference being that cereal-based samples only included wheat samples and tomato-based samples were tomato purees during the second procedure. In the case of TEA, HorRat values ranged from 0.40 to 0.66 with IDMS detection in unpeeled samples, while the value was 0.53 in peeled samples, which is significantly better than the values without ISTD (Table 4). As previously expected, isotope dilution greatly improved inter-laboratory reproducibility. During the validation, the expected precision could only be achieved with ISTDs, which is common in LC-MS quantitative studies. This is always due to matrix effect compensation.

Table 5. Ion transition values of Alternaria toxins and the isotope labelled ISTDs using negative ESI

7. Documentation

The full validation dossier was completed by 2020 [12], together with the draft standard. Review and revision of the draft standard will be completed soon and the proposed standard is expected to be adopted by CEN in the end of 2021 (the standard has been issued since the article was submitted: CEN EN 17521:2021 Foodstuffs - Determination of Alternaria toxins in tomato, wheat and sunflower seeds by SPE clean-up and HPLC-MS/MS. The Editor).

8. Deviation from the standard method

LC-MS/MS instruments from different vendors may vary significantly in terms of sensitivity. One of the main reasons for this is the ion source [11]. While the standard describes the use of ESI (Electrospray Ion Source), there is hardly any application in the literature where the ESI ion source of the instrument showed sufficient efficiency to achieve the desired detection limit, so the use of atmospheric pressure chemical ionization (APCI) became necessary [13]. Another possibility is when the instrument used is so sensitive that no solid phase purification or enrichment (Solid Phase Extraction – SPE) is required, but the extract of the sample can be injected directly into the device („dilute-and-shoot”) [11], [14]. The important feature of a standard is that all laboratories should be able to use the method described in it, so the application of SPE enrichment was unavoidable due to the low concentration levels and the complexity of the unpeeled sunflower seed samples.

If the first validation is successful, matrix-matched calibration would probably be recommended by the standard. However, with the advent of ISTDs, a group of laboratories would prefer to use IDMS later on. From this point of view, it is fortunate that IDMS has been introduced in the standard, which is simpler and more accurate, but the acquisition of ISTDs is more expensive. In the absence of ISTDs, standard addition (as a quantitative evaluation) can also be used to adequately compensate for the matrix effect, but this is time-consuming, because each sample must be prepared at least four or five times. Yet there are laboratories that use this type of evaluation.

9. Acknowledgment

I would like to give thanks Carlos Gonçalves for the successful completion of the standardization project.

10. References

[1] A Bizottság 1881/2006/EK rendelete (2006. december 19.) az élelmiszerekben előforduló egyes szennyező anyagok felső határértékeinek meghatározásáról. Az Európai Unió Hivatalos Lapja, L 364/5. (Hozzáférés: 2021.04.12)

[2] EFSA, European Food Safety Authority, (2011): Scientific Opinion on the risks for public and animal health related to the presence of citrinin in food and feed, EFSA J. 10 p. 1-82. DOI

[3] CEN/TR 16059, Food analysis. Performance criteria for single laboratory validated methods of analysis for the determination of mycotoxins.

[4] Tölgyesi, Á., Stroka, J. (2014): Report on the development of a method for the determination of Alternaria toxins and citrinin in wheat, tomato juice and sunflower seeds by liquid chromatography – tandem mass spectrometry. JRC Technical report (Hozzáférés: 2021.02.24)

[5] Asam, S., Liu, Y., Konitzer, K., Rychlik, M. (2011): Development of a stable isotope dilution assay for tenuazonic acid, J. Agr. Food Chem. 59 p. 2980–2987. DOI

[6] Lau, BP-Y, Scott, P.M., Lewis, D.A., Kanhere, S.R., Cleroux, C., Roscoe, V.A. (2003): Liquid chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry of the Alternaria mycotoxins alternariol and alternariol monomethyl ether in fruit juices and beverages. J Chromatogr A. 998 p. 119–131. DOI

[7] Tölgyesi, Á., Stroka, J., Tamosiunas, V., Zwickel, T. (2015): Simultaneous analysis of Alternaria toxins and citrinin in tomato: an optimised method using liquid chromatography-tandem mass spectrometry, J. Food Addit. Contam. 32 p.1512–1522. DOI

[8] Tölgyesi, Á., Stroka, J. (2016): Collaborative study report: Determination of Alternaria toxins in cereals, tomato juice and sunflower seeds by liquid chromatography tandem mass spectrometry, JRC Technical Report (Hozzáférés: 2021.03.14)

[9] Practical guide to ISO 5725-2:1994 — Accuracy (trueness and precision) of measurement methods and results — Part 2: Basic method for the determination of repeatability and reproducibility of a standard measurement method. Geneva, Switzerland.

[10] Thompson, M., Ellison, S.L.R., and Wood, R. (2006): The International Harmonised Protocol for the Proficiency Testing of Analytical Chemistry Laboratories. Pure Appl. Chem. 78(1):145–196.

[11] Tölgyesi, Á. (2021): Gyakorlati példák a folyadékkromatográfiával kapcsolt hármas kvadrupol rendszerű tandem tömegspektrometria élelmiszer-, bio- és textilanalitikai alkalmazására, Kromatográfus különszám, Gen-lab Kft., Budapest, Magyarország (Hozzáférés: 2021.02.07)

[12] Gonçalves, C., Tölgyesi, Á., Bouten, K., Robouch, P., Emons, H., Stroka, J. (2021): Determination of Alternaria toxins in tomato, wheat and sunflower seeds by SPE and LC-MS/MS – a method validation through a collaborative trial, J. AOAC Inter. 1-15. DOI

[13] Tölgyesi, Á., Kozma, L., Sharma, V.K. (2020): Determination of Alternaria toxins in sunflower oil by liquid chromatography isotope dilution tandem mass spectrometry, Molecules 25, 1685. DOI

[14] Tölgyesi, Á., Farkas, F., Bálint, M., McDonald, T.J., Sharma, V.K (2021): A dilute and shoot strategy for determining Alternaria toxins in tomato-based samples and in different flours using LC-IDMS separation, Molecules 26, 1017. DOI

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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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Examination of the nutrient content and color characteristics of honey and pollen samples

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Examination of the nutrient content and color characteristics of honey and pollen samples

DOI: https://doi.org/10.52091/EVIK-2022/1-4-HUN

Received: December 2021 – Acceptes: February 2022

Authors

1 Hungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology

Keywords

honey, pollen, nutrient content, botanical origin, moisture content, sugar content, ash content, amino acid composition, HMF, color characteristics

1. Summary

Due to its nutritional value, physiological effects and unique aroma, honey is one of our widely consumed foods, used for sweetening. There are several regulations concerning the composition and analysis of honey, of which the specifications and guidelines of the Hungarian Food Codex are authoritative in Hungary. In the present study, the color characteristics and nutrient composition of domestic and foreign honeys are examined. Our intention was to review the physical and chemical characteristics of honeys of different origin marketed in Hungary. As a point of interest, a honey obtained from a foreign market was also examined. Pollen is a less widely consumed apiculture product, mostly a dietary supplement known to health-conscious consumers. There is also much less knowledge is available about its composition than in the case of honey. With our work, we intended to fill this gap. In addition, the nutrient content and color characteristics of pollen samples from some plant species that also occur in Hungary are described.

2. Introduction

Honey is one of our oldest foods and is still a popular sweetener around the world. According to the definition of the Hungarian Food Codex, „Honey is a natural sweet substance collected by Apis mellifera bees from plant nectar or the sap of live plant parts, or by insects that suck plant sap from the secreted material of live plant parts, which is collected by the bees, converted by the addition of their own substances, then stored, dehydrated and matured in honeycombs” [1]. Its energy content is provided by easily absorbed carbohydrates, but it also contains many other nutrients such as minerals, phenolic compounds and amino acids. Thanks to its natural aroma substances, honey has pleasant organoleptic properties, so it can be characterized by a high level of enjoyment [2]. Honey is also used for medicinal purposes, mainly due to its anti-inflammatory and antibacterial effects [3].

The pollen cluster is a little-known apiculture product that is of growing interest, especially among health-conscious consumers. The pollen cluster is formed by bees moistening the pollen adhering to their bodies with nectar and their glandular secretions, then compacting it into spherical pellets and transporting them to their hives in their “baskets” on their hind legs. This product can be collected by the beekeeper using a perforated device mounted in front of the hive entrance [4]. The product is usually preserved by drying or freezing. Pollen contains relatively high concentrations of nutrients essential for the body and can therefore be used as a dietary supplement [5] or a functional food raw materials [6]. According to some research, pollen has immunostimulatory and antioxidant effects, and thus plays an important role in apitherapy [3]. As the demand for apiculture products (honey, pollen, bee bread, propolis, wax) has increased, so has the number of scientific studies on honey. The number of studies on honey and pollen has increased exponentially since the 1990s [7]. From a food safety point of view, apiculture products have been the focus of research, as they may contain a number of risk factors, including pesticides, toxic metals, molds, mycotoxins, pyrrolizidine alkaloids, allergens, genetically modified organisms, and so on. The food safety risks of pollens are presented in detail in the review article of Végh et al. [8].

There is a tradition of beekeeping in Hungary, as the climatic and landscape conditions of the Carpathian Basin allow the production of high quality honey. Bees visit more than 800 plant species, several of which are suitable for the production of singe flower honey [9]. The two main products of the domestic honey market are mixed flower honey and acacia honey. The latter is considered a hungaricum, as there are large acacia forests in Hungary, and acacia honey is a high quality, sought-after product both at home and abroad [10]. The production of rapeseed and sunflower honey is widespread throughout the country, but smaller amounts of other single flower honeys such as chestnut, linden, phacelia, hawthorn, goldenrod, lavender, buckwheat and milkweed honey are also produced by Hungarian beekeepers. In addition to honey, other apiculture product also add color to the product range of beekeepers, of which one of the most popular is pollen cluster.

Examining export and import data, Mucha et al. proved that Hungary has a comparative advantage in the European Union in terms of honey production [11]. A significant part of the total honey production of the EU comes from Hungary, which, in addition to environmental conditions, is due to the relatively high bee density of the Carpathian Basin. The number of bee colonies is constantly increasing, which also indicates the effectiveness of the National Beekeeping Programs. Nevertheless, it is a serious challenge for the sector that Hungarian honey is behind world competitors in the price competition, especially compared to lower quality honey from China [11, 12]. According to the in-depth interviews of Oravecz and Kovács with consumers, Hungarian honey buyers can be divided into two distinct groups based on where they get their product: some consumers buy only from primary producers, while other look for readily available, cheaper products online or on store shelves [13]. According to the majority of the consumers surveyed, honey from Hungarian producers is not only more reliable, but also tastes better and is healthier than imported honey.

The regulation of honey quality is dealt with in the Hungarian Food Codex (Codex Alimentarius Hungaricus): specification 1-3-2001/110 contains the definitions and compositional requirements of honeys, guideline 2-100 the requirements and characteristics of honey types with a distinctive quality mark, while guideline 3-2-2009/1 the sampling and analytical methods of honey [1, 14, 15]. The Hungarian Food Codex does not cover the quality requirements of other apiculture products. There are currently no specific regulations for pollen clusters at the international level, however, product standardization was initiated by one of the working groups of the International Organization for Standardization, Technical Committee Food Products, Subcommittee Bee Products (ISO/TC34/SC19/WG 3) in 2018 [16].

The nutritional value and organoleptic properties of honeys and pollen are mainly determined by the botanical origin, but are also influenced by the geographical origin, the climatic conditions of the collection area, the bee species producing the product, as well as the processing and storage conditions [2, 3, 5, 9, 17]. In our research, domestic and foreign honeys of different plant origin were compared, based on their moisture, reducing sugar, ash, free amino acid and hydroxymethylfurfural (HMF) contents and pH. Our work also included the study of the macronutrient composition of pollen clusters from plants typical of the Hungarian flora. The color of honey and pollen samples was also investigated, as this property plays an extremely important role in the consumer perception of foods and in consumer decisions [6, 18].

3. Materials and methods

3.1 Samples examined

The products involved in the study included eight honey from Hungary and eight honeys from abroad. The plants indicated as the nectar sources of the Hungarian honeys were acacia, linden, chestnut, goldenrod, rapeseed and phacelia, and a forest (honeydew) honey and a mixed flower honey were also included in the study. The foreign samples included products that are considered specialties in Hungary such as thyme (Spain), wild lavender (Portugal), coriander (Bulgaria), buckwheat (EU), larch (Czech Republic), coffee flower (Guatemala) and orange blossom honey (Mexico), and a mixed flower honey from Ghana. These products were purchased in a specialty store in Budapest, while the mixed flower honey from Ghana was obtained from the market in the country of origin. The pollen clusters used in the study were purchased from Hungarian beekeepers and stores. The products were dried at 38±2 °C for 20 hours, and then ten subsamples were formed by color sorting and their botanical composition was determined. Honey and pollen samples were stored at room temperature (20±2 °C) in the dark.

3.2 Methods used

An Abbe refractometer was used to determine the moisture content of the honeys [19]. Reducing sugar content was determined by the Schoorl-Regenbogen method [20], while ash content was determined by incineration [21]. The determination of the free amino acid content was carried out with an INGOS AAA 400 amino acid analyzer. HMF content was measured by the method of White [22, 23]. to determine the pH value of the honeys, a Radelkis universal pH meter (OP-204/1) was used [24]. The botanical origin of the pollen clusters was determined by microscopic pollen analysis. Moisture content of the samples was analyzed by the vacuum drying method [25]. To determine the protein content, the classical Kjeldahl method was used. Crude fat content was determined by Soxhlet extraction [25]. Ash content was determined by incineration [26], while the following formula was used to calculate the carbohydrate content:

Carbohydrate(%) = 100 - Moisture(%) - Protein(%) - Raw fat(%) - Ash(%)

Color characteristics of the honeys and pollen were examined with a Minolta CR-100 instrument. The results are expressed with the coordinates of the CIE-Lab color space, where „L” is the perceptual lightness, while „a*” and „b*” are values for red-green and blue-yellow colors respectively. Each analysis was carried out in three parallel measurements.

4. Results and evaluation

4.1 Honey test results

4.1.1. Moisture content

Moisture content is one of the most basic parameters determining the quality of honey, which affects the viscosity, color, taste and crystallization of the product, as well as significantly affecting its shelf life. The moisture content of honeys generally varies between 15 and 21%, depending on the species of the source plant, the dehydration processes taking place in the hive and the way the honey is processed and stored [17]. Honeys produced in a dry, warm environment generally have a lower moisture content than those coming from countries with cool, humid climates [27]. According to specification 1-3-2001/110 of the Hungarian Food Codex, the moisture content of honeys must not exceed 20% [1].

The moisture content of the honey samples examined by us ranged from 17.5 to 21.8% (Figure 1). Of the honeys originating from Hungary, the moisture content of rapeseed honey and mixed flower honey, and of the foreign honeys, the moisture content of buckwheat honey exceeded the current limit value in Hungary. According to Czipa et al., a moisture content above the permissible limit indicates that the bees were not able to thicken he honey properly due to heavy carrying, so these honeys should be considered immature [28]. However, the water absorption capacity of the honeys is also influenced by their botanical origin, so the high moisture content of buckwheat honey may be traced back to this.

Figure 1. Moisture content of the honey samples
Mixed flower M: mixed flower honey, Hungary; mixed flower G: mixed flower honey, Ghana

4.1.2. Reducing sugar content

Approximately 95% of the dry matter content of honey consists of carbohydrates, of which simple reducing sugars are present in high concentrations: fructose accounts for 32-44% of the weight of honey, while glucose accounts for 23-38% [29]. The fructose and glucose present in honey are derived from the sucrose content of the nectar through the action of the enzyme invertase produced by the bees [2, 9]. According to the Hungarian Food Codex, flower honeys must have a fructose and glucose content of at least 60%, while forest (honeydew) honeys at least 45% [1]. Smaller amounts of various disaccharides, oligosaccharides and polysaccharides may also be present in the products. Lower reducing sugar and higher sucrose contents may be characteristic of the plant, but may also indicate the immaturity of the honey or the feeding of bees with sugar syrup [28, 29].

The reducing sugar content of the samples examined by us ranged from 64.50 to 75.25% (Figure 2). Foreign samples contained 3% more reducing sugars than Hungarian honeys on average. The highest value was obtained for wild lavender honey, while the lowest was obtained for forest (honeydew) honey. According to literature data, it is a special feature of honeydew honeys that they contain higher proportions of complex sugars, mainly raffinose and melezitose, than honeys made from flower nectar [30].

Figure 2. Reducing sugar content of the honey samples
Mixed flower M: mixed flower honey, Hungary; mixed flower G: mixed flower honey, Ghana

4.1.3. Ash content

According to literature data, the ash content of honeys of nectar origin is generally between 0.02 and 0.3%, while forest (honeydew) honeys contain inorganic substances in a concentration of about 1% [29]. The amount of minerals depends on the geographical and botanical origin of the honey, the composition of the soil and the extent of contamination in the vicinity of the source plant, so honey can also be considered an environmental bioindicator [31]. According to research, the ash content of dark-colored honeys is generally higher than that of lighter honeys [17, 32]. Our results (Figure 3), in line with literature data, showed that forest honey contains an outstanding amount (0.97%) of minerals. Of honeys of nectar origin, larch, goldenrod and linden had an ash content of more than 0.3%. Acacia, rapeseed, phacelia, mixed flower from Ghana, thyme and orange blossom honeys on the other hand had relatively low levels of inorganic matter, less than 0.1%. No close correlation was observed between the color and ash content of the products. The forest, larch and goldenrod honeys with the highest ash content were dark in color, but linden and chestnut honeys, despite their high mineral content, were characterized by a light color. Buckwheat honey and the mixed flower honey from Ghana had a very dark color and a low ash content.

Figure 3. Ash content of the honey samples
Mixed flower M: mixed flower honey, Hungary; mixed flower G: mixed flower honey, Ghana

4.1.4. Amino acid composition

Some of the amino acid content of honeys comes from the nectar or the pollen, according to which the amino acid composition may be an indicator of botanical origin [29, 33, 34]. Nevertheless, free amino acids also enter honey as a result of bee secretion processes, which increases the variability in the amino acid content of honeys from the same source plant [35]. The amino acid composition of nectar, and thus of honey, is also affected by the time of the year it is collected by the bees: in spring, when the trees are budding, and in autumn, when the color of the leaves changes, the concentration of amino acids and nitrogen containing compounds in the phloem increases significantly [36]. The variability of the amino acid content of the same type of honey is also increased by the fact that their amount decreases during storage [37] and upon heat treatment [38].

Most amino acids are present in honey in bound form. The free amino acid content accounts for approximately one-fifth of the total amino acid content [29]. Proline makes up 50-85% of the amino acids present, the amount of which decreases continuously during storage, so it can also be an indicator of the aging of honey [39]. Some of the proline enters the honey due to secretion processes in the bees [9], while another part is of plant origin, as both nectar [40] and pollen [5] have a high proline content. There is no clear regulation of its amount in Hungary, so the minimum limit value of 180 mg/kg in force in Germany is generally taken into account [39].

The average free amino acid concentration in the honeys studied by us was 663.3 mg/kg. Foreign honeys had a slightly higher average amino acid content (787.6 mg/kg) than products from Hungary (539.0 mg/kg). The concentration of free amino acids in coriander, wild lavender and goldenrod honey exceeded 1,000 mg/kg, while in acacia honey only 162.2 mg/kg was detected (Table 1). Research has sown that acacia honeys are generally characterized by a relatively low amino acid content [33, 41]. The high amino acid content of goldenrod honey can be traced to the fact that the flowering period of the plant can last from August to the end of October.

The amount of proline was remarkably high in all samples. In addition, most honeys had relatively high levels of aspartic acid, glutamic acid, asparagine, glutamine and phenylalanine. Relatively high levels of serine, alanine, valine and tyrosine were observed in some samples. Buckwheat honey had extremely high methionine, threonine and valine contents, while wild lavender honey had outstanding amounts of phenylalanine, tyrosine and arginine.

Table 1. Free amino acid composition of honey samples

*Mixed flower honey, Hungary
** Mixed flower honey, Ghana

Figure 4 shows the ratio of proline to the amount of total free amino acids. According to Hermosín, proline accounts for at least two-thirds of the amino acid content of fresh honey [34]. Half of the honeys examined by us showed a lower proline ratio. Of domestic honeys, the proline ratio was 56% in mixed flower honey, while it was less than 66% in all foreign honeys, with the exception of coriander honey. Average proline content values were determined by Kaskoniené and Venskutonis for single-flower honeys of great economic importance in Europe, taking into account hundreds of test results per variety. Based on their results, thyme (Thymus spp.) honeys have an outstanding proline content (956±196 mg/kg), however, the thyme honey examined by us contained relatively little proline [33]. The average proline content of acacia (Robinia pseudacacia L.) honeys was approximately twice the value detected by us. Linden (Tilia spp.), chestnut (Castanea sativa Miller) and forest (honeydew) honeys had on average 20-30% proline contents than the samples examined by us. Concentrations similar to those reported by the authors were obtained by us for rapeseed (Brassica napus L.) honey. Of the honey samples, coriander honey had the highest proline content (943.8 mg/kg), but this was significantly lower than the value (2,283 mg/kg) reported by Czipa [9]. The differences are presumably due to the longer storage time. With the exception of the acacia honey, the mixed flower honey from Ghana and the coffee flower honey, all products complied with the minimum value of 180 mg/kg required in Germany.

Figure 4. Proline content of honey samples compared to the total amino acid content
Mixed flower M: mixed flower honey, Hungary; mixed flower G: mixed flower honey, Ghana

4.1.5. Hydroxymethylfurfural content

Hydroxymethylfurfural (HMF) is formed in an acidic medium by the decomposition of hexoses. The maturity of honey can be inferred from its concentration, since this compound is present in minimal amounts in fresh honey. HMF content increases during the heating and storage of honey, but high acid, moisture and sugar contents also accelerate its formation [9, 29]. Its concentration also depends on the type of honey: tropical and subtropical honeys from warm environments have inherently high HMF content [27]. Specification 1-3-2001/110 of the Hungarian Food Codex prescribes a limit of 40 mg/kg for honeys in general, while the limit value is 80 mg/kg for honeys of tropical origin [1].

The HMF content of the honeys examined by us varied widely (Figure 5): its concentration was only 3.98 mg/kg in acacia honey, while the mixed flower honey from Ghana had an extremely high HMF content (140.42 mg/kg). All honeys from Hungary complied with the limit value in force. Of foreign honeys, the mixed flower honey from Ghana significantly exceeded the limit set for tropical honeys. According to its tropical origin, the coffee flower honey from Guatemala can also be characterized by a high HMF content (64.41 mg/kg).

Figure 5. Hydroxymethylfurfural content of honey samples
Mixed flower M: mixed flower honey, Hungary; mixed flower G: mixed flower honey, Ghana

4.1.6. pH

The pH of honeys is usually below 6, mainly due to the organic acids found in them. The amount of organic acids is less than 0.5%, but they significantly affect the color, aroma and shelf life of the product. Certain acids (e.g., citric acid, malic acid, oxalic acid) come from nectar and honeydew, while others (e.g., formic acid) are formed by enzymatic processes during maturation and storage [29]. A significant proportion of the organic acids in honey is gluconic acid, which is formed from glucose by the enzyme glucose oxidase. The pH of honey does not depend directly on the amount of organic acids, which is mainly due to the honey components with buffer capacity [9].

The pH of the honey samples examined by us varied between 2.85±0.02 and 4.60±0.04 (Figure 6). The lowest value was obtained for phacelia honey, while the highest value was measured for forest (honeydew) honey. Our results support the finding of Tischer Seraglio et al. that the pH of honeydew honeys is relatively high, generally between 3.8 and 4.6 [30]. This is due to the fact that the minerals and amino acids in them buffer the acidic pH [9].

Figure 6. pH of the honey samples
Mixed flower M: mixed flower honey, Hungary; mixed flower G: mixed flower honey, Ghana

4.1.7. Color characteristics

The color of honey is an important organoleptic parameter, as it significantly influences consumer decisions. In most countries, high quality is associated with light honey, but in Germany, Switzerland and Greece, for example, darker products are more popular. Honey ranges in color from colorless to dark amber, sometimes with a greenish or reddish tinge. The color of honey is influenced, for example, by the plant and geographical origin, climatic conditions, soil condition of the source plant, storage time, exposure to light, possible heat treatment, certain enzymatic reactions and crystallization processes [17, 29]. This property is related to, among other things, moisture content and the concentrations of minerals, carotenoids, phenolic compounds and sugars [18].

The values of L (brightness), a* (green-red color) and b* (blue-yellow color) obtained for the honeys examined by us were plotted on a three-dimensional diagram (Figure 7). The darkest samples were the buckwheat honey and the mixed flower honey from Ghana, while the lightest were the acacia, linden and phacelia honeys. Based on the a* value, most of the honeys were more or less reddish in color, but the acacia, linden and phacelia honeys exhibited a very slight greenish hue. In several cases, an inverse relationship was observed between the lightness value and HMF content of the honeys: goldenrod honey, forest honey and the mixed flower honey from Ghana were characterized by relatively low L values and high HMF content, while the lightest honeys had low HMF content. The reason for this is that some of the HMF is formed during the Maillard reaction [9, 17].

Figure 7. L, a* and b* values of the honey samples
Mixed flower M: mixed flower honey, Hungary; mixed flower G: mixed flower honey, Ghana

4.2. Test results of pollen clusters

4.2.1. Botanical origin

The results of the microscopic pollen analysis confirmed that the pollen clusters used in the research had a lead pollen content of more than 80%, i.e. they could be considered monofloral [42]. The pollen cluster samples are shown in Figure 8, and their pollen composition is summarized in Table 2.

Figure 8. Monofloral pollen cluster samples
Table 2. Botanical composition of the pollen cluster samples

4.2.2. Macronutrient composition

The nutritional value of the pollen clusters showed great heterogeneity, as the proportion of nutrients is significantly influenced by the botanical origin. Summarizing the results of more than one hundred scientific studies, Thakur and Nanda concluded that the products contained an average of 54.2% (18.5-84.3%) carbohydrates, 21.3% (4.5-40.7%) protein, 5.3% (0.4-13.5%) lipid and 2.9% (0.5-7.8%) ash [5]. Their moisture content in the fresh state was between 20 and 30%. Dried products, in an optimal case, contained 4-8% water, as this range is suitable from both a food safety and organoleptic point of view [43].

The pollen clusters analyzed had a moisture content between 4.9 and 8.2%, which ensures adequate microbiological stability. The carbohydrate content of our samples was on average 12% higher than the average value reported by Thakur and Nanda [5]. The difference is mainly due to the fact that, when examining the average concentration, the authors took into account the results obtained not only for dried but also fresh pollen. The protein content of the samples ranged from 14.5 to 26.7%. The most protein-rich pollen clusters came from phacelia and rapeseed, which are strong attractants for bees [43]. In terms of crude fat content, dandelion pollen, also preferred by bees, exhibited outstanding concentrations, but rapeseed pollen was also found to be rich in lipids. The ash content of the pollen clusters ranged from 1.0 to 3.2%. The most minerals were contained in the samples from musk thistle and cherry. Our results (Table 3) are consistent with literature data [5, 42].

Table 3. Macronutrient composition of the pollen cluster samples

4.2.3. Color characteristics

The color of pollen clusters from different plants varies widely: they are most often yellowish and orange in color, but there are also blue, green, red, black, brown and white pollens [44]. The color of pollens is primarily determined by their botanical origin. Since bees usually collect pollen from a single plant species at a given time, each pollen cluster can be characterized by a homogeneous color [4]. The color characteristics of the product are also affected by the geographical origin, climatic conditions, the time of collection, the age and nutrient supply of the source plant, the preservation method of the pollen, as well as the duration and conditions of storage [6].

The values of L (brightness), a* (green-red hue) and b* (blue-yellow hue) obtained for the pollen clusters are shown in Figure 9. The darkest samples were musk thistle, phacelia and common poppy, the other samples had relatively high L values. The light samples can be divided into three groups based on their a* values: rapeseed, cherry and blackberry pollens had a slight greenish tinge, old man’s beard was slightly reddish, while rockrose, sunflower and dandelion exhibited a stronger reddish hue. The value of b* was positive in all cases, indicating that the yellow color dominated in the samples. Phacelia pollen, which is relatively common in the domestic market, is strikingly dark in color. This pollen is characterized by a lighter shade of yellow compared to the also dark common poppy, and a weaker shade of red compared to the musk thistle.

Figure 9. L, a* and b* values of the pollen cluster samples

5. Summary

In the course of our research, domestic and foreign honeys were compared on the basis of the parameters determining their quality, and the macronutrient composition and color characteristics of several pollen clusters from the plants characteristic of the flora of the Carpathian Basin were also determined. Of the honeys examined, the moisture content of two Hungarian and one foreign sample exceeded the limit value in force in Hungary. The reducing sugar content of the honeys ranged from 64.5 to 75.3%. Our results support the observation that honeydew honeys have a lower reducing sugar content, a higher ash content and pH, and can be characterized by a darker color than nectar-derived honeys. Proline was the dominant amino acid in the honeys, but its proportion was lower in several cases than the values reported in the literature. Of domestic honeys, the proline content of acacia honey and mixed flower honey did not reach the minimum limit of 180 mg/kg, while in the case of foreign honeys, the same was true for the coffee flower honey. In terms of the HMF content, large differences were observed. All of the domestic honeys met the requirements, but the mixed flower honey from Ghana contained an extremely high concentration of this compound. The color yellow dominated the honeys. Most of the products could be characterized by a reddish hue, but some of the honey samples had a slightly greenish tinge. In several cases, an inverse relationship was observed between the brightness value and the HMF content of the honeys.

By examining the botanical composition of the dried pollen clusters included in the study, it was confirmed that least 80% of the samples used were from the plant species named as the source plant. In line with literature data, the products contained 57.9-74.0% carbohydrates, 14.5-26.7% protein, 1.4-10.5% cruse fat and 1.0-3.2% ash. Their moisture content ranged from 4.9 to 8.2%, which meets the requirements from both an organoleptic and microbiological point of view. In terms of their color characteristics, the products exhibited great variation, but in most cases the yellow hue dominated their color.

6. Acknowledgment

Kutatásunk az EFOP-3.6.3-VEKOP-16-2017-00005 projekt, valamint az „OTKA” Fiatal kutatói kiválósági program (FK_20, azonosítószám 135700) segítségével valósult meg. A szerzők köszönik Rőzséné dr. Büki Etelka segítségét a virágporcsomók botanikai eredetének meghatározásában.

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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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[12] Balagozian, E. A., Pravdivtseva, O. E., Orekhova, A. D., Kurkin, V. A. (2016a): A comparative phytochemical analysis of raw materials of stinging nettle and its main impurities. Questions of Biological, Medical and Pharmaceutical Chemistry, 12, pp. 15-18.

[13] Balagozian, E. A., Pravdivtseva, O. E., Orekhova, A. D., Kurkin, V. A. (2016b): A comparative phytochemical analysis of raw materials of stinging nettle and its main impurities. Questions of Biological, Medical and Pharmaceutical Chemistry, 12, pp. 15-18.

[14] Pekh, A. A. (2019): The content of micronutrients in stinging nettle depending on the habitat in the Republic of North Ossetia-Alania. News of the Mountain State Agrarian University, 2, pp. 38-41.

[15] Tatvidze, M. L., Kupatashvili, N. N. (2018): A study of some biologically active substances of dry leaves of stinging nettle. Theoretical and Applied Science, 6 (62), pp. 157-161. DOI

[16] Trineeva, O. V., Safonova, E. F., Slivkin, A. I. (2017): The validation of the method for determining ascorbic acid using high performance thin-layer chromatography. Sorption and Chromatographic Processes, 3, pp. 414-421.

[17] Guskov, A. A., Rodionov, Yu. V., Anokhin, S. A., Glivenkova, O. A., Plotnikova, S. V. (2018): The technology of the vacuum-pulse extraction of soluble substances from nettle and hops. Innovative Engineering and Technology, 2(15), pp. 23-27.

[18] Kalinkina, O. V., Sychev, I. A. (2017): The influence of stinging nettle polysaccharide on blood and blood formation. Bulletin of Tver State University. Series: Biology and Ecology, 1, pp. 62-68.

[19] Korzh, L. (2017): Enriching the rations of laying hens. Animal Breeding of Russia, 4, pp. 17.

[20] Filippova, O. B., Frolov, A. I., Maslova, N. I. (2019): The biological basis for the stimulation of the resistance of calves using the modern technology for dairy cattle breeding. Science in Central Russia, 1(37), pp. 61-70.

[21] Zhitnikova, Yu. Zh. (2004): Rabbits: breeds, breeding, management, care. Rostov-on-Don, Fenix, pp. 256.

[22] Ryadchikov, V. G. (2012): The basics of nutrition and feeding of farm animals. Krasnodar, Kuban State Agrarian University, pp. 328.

[23] Viktorov, P. I., Menkin, V. K. (1991): Methodology and organization of livestock experiments. Moscow, Agropromizdat, pp. 112.

[24] Zabelina, M. V. (2014): Research methods in private zootechnics. Saratov, Saratov State Agrarian University, pp. 60.

[25] Kalashnikova, A. P., Fisinina, V. I., Scheglova V. V., Kleimenova, N. I. (2003): Norms and rations of feeding farm animals. Reference manual. 3rd revised and enlarged edition. Moscow, Russian Agricultural Academy, pp. 456.

[26] Kirilov, M. P., Makhaev, E. A., Pervov, N. G., Puzanova, V. V., Anikin, A. S. (2008): Methodology for calculating the exchange energy in fodders based on the content of crude nutrients. Dubrovitsy, All-Russia Research Institute for Animal Husbandry of the Russian Agricultural Academy, pp. 382.

[27] Balakirev, N. A., Nigmatulin, R. M., Sushentsova, M. A. (2015): Fodders and feeding rabbits. Moscow, Kazan, Nauchnaya Biblioteka Publishing House, pp. 268.

[28] Kahikalo, V. G., Nazarchenko, O. V., Balandin, A. A. (2019): A practical guide to fur farming and rabbit breeding. St. Petersburg, Lan Publishing House, pp. 328.

[29] Antipova, L. V., Glotova, I. A., Rogov, I. A. (2001): Methods of studying meat and meat products. Moscow, Kolos, pp. 376.

[30] Gotsiridze, N., Tortladze, L. (2001): Determination of the biological value of rabbit meat. Zootechnics, 8, pp. 31-32.

[31] Martinchik, A. N., Maev, I. V., Yanushevich, O. O. (2005): General nutritionology. Moscow, Medicine, pp. 392.

[32] Pronin, V. V., Fisenko, S. P. (2018): Veterinary and sanitary expertise with the basics of technology and standardization of animal breeding products. St. Petersburg, Lan Publishing House, pp. 240.

[33] Vasilieva, L. A. (2007): Statistical methods in biology, medicine and agriculture. Novosibirsk, Novosibirsk State University, pp. 320.

[34] Yudenkov, V. A. (2013): Variance analysis. Minsk, Business offset, pp. 76.

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Acrylamide content of commercially available capsule coffees

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Acrylamide content of commercially available capsule coffees

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

Received: August 2021 – Accepted: November 2021

Author

1 National Food Chain Safety Office, Food Chain Safety Laboratory Directorate, National Analytical Reference Laboratory

Keywords

Acrylamide, carcinogenic, toxic compound, asparagine, Maillard reaction, capsule coffee, orol coffee, powdered coffee, robusta, arabica, effect of powdering processes, caffeinated and decaffeinated coffee, LC-MS/MS

1. Summary

The consumption of capsule coffees is becoming more and more common in everyday life. Today, a number of studies support the fact that there are benefits of consuming the right amount of coffee. Despite its beneficial effects, there are also disadvantages to drinking coffee. For example, the acrylamide found in roasted coffee, which is formed during the process of roasting, poses a health risk. Acrylamide has been classified by the International Agency for Research on Cancer (IARC) as a Group 2A substance, i.e., as an agent which is probably carcinogenic to humans [1]. The technological parameters of the roasting process affect the amount of acrylamide formed in the product. Light roasted coffees contain higher levels of this compound than dark roasted coffees.

Numerous studies have been conducted to investigate the acrylamide content of ground coffee products, however, capsule coffees have not yet received similar attention in this respect. In my study, the acrylamide content of various types of commercially available capsule coffees was investigated by HPLC-MS/MS measurements. Decaffeinated coffees are produced using a different technology, so some of these types were also tested.

2. Introduction

2.1. Acrylamide, its formation and effects

Acrylamide is an organic compound with the molecular formula C3H5NO. Its IUPAC name is prop-2-enamide. It is a low molecular weight, odorless, white solid which is highly soluble in water but also soluble in organic solvents. It is used in industry in the production of polyacrylamides, which are used as water-soluble thickeners and flocculants. It is a highly toxic compound therefore it is mainly handled in the form of an aqueous solution [2].

Acrylamide is a human neurotoxin, classified by the International Agency for Research on Cancer (IARC) as a Group 2A substance, i.e., as an agent which is probably carcinogenic to humans [1]. Acrylamide has been used in many industrial processes since the 1950s. An announcement was issued by the Swedish National Food Administration on April 24, 2002, about the discovery that it is formed as a byproduct in heat-treated foods with a high carbohydrate content [3] and can therefore be detected mainly in snack foods, potato chips, breads, cereal products and coffee. Following the discovery, more and more studies were launched to detect acrylamide content. An increasing number of researchers are looking for the answer to the question how it is formed in different foods.

Mottram et al. have conducted extensive studies on the formation of acrylamide from amino acids and reducing sugars during heat treatment as a result of the Maillard reaction. Asparagine, the amino acid most abundant in potatoes and cereals, has been found to contribute greatly to acrylamide formation. During baking and roasting, products of the Maillard reaction are responsible for the formation of flavor and aroma substances and the development of color. Strecker degradation of the amino acids also occurs at this time, during which amino acids are decarboxylated and then deaminated to form aldehydes. An outline of the process is shown in Figure 1 [4].

According to several studies, acrylamide is toxic because it forms adducts with compounds found in hemoglobin and also reacts with important functional proteins and DNA. Glycidamide, a metabolite of acrylamide, reacts similarly with hemoglobin as well [5].

The area most studied is related to the neurotoxic properties of acrylamide, since these can be observed in both humans and animals. Observations have been made in a variety of laboratory animals, including cats, rats, mice, rabbits and monkeys. After administration of 0.5 to 50 mg acrylamide/kg/day, limb movement disorders and muscle weakness could be observed in all animals [6].

Figure 1. Outline of acrylamide formation [4]

2.2. Acrylamide in coffee

The acrylamide content of coffee is formed during roasting. In an extensive study, Guenther et al. found that it is produced in the highest amount (more than 7 mg/kg) during the initial stage of roasting, and then the amount decreases towards the end of the process. Towards the end of the roasting cycle, acrylamide is increasingly eliminated, with both physical and chemical losses [7].

Kinetic models and other experiments with isotopically labeled acrylamide have shown that more than 95% of the acrylamide formed is degraded during the entire roasting process. This means that the acrylamide content of lightly roasted coffees with a shorter roasting cycle is much higher than that of dark roasted beans [7].

The authors of the study also explained that green coffee beans contain very low concentrations of asparagine (0.2–1.0 g/kg), which is only negligibly higher in the case of Robusta species. Thus, it was found that the amount of asparagine and the acrylamide concentration showed a weak correlation, and even no correlation was found in Robusta beans. This is due to the fact that the rate of acrylamide loss far exceeds the rate of its formation [7].

Alves et al. studied how the acrylamide content in brewed espresso coffee changes, as in their opinion it most often enters consumers’ body in this form. Acrylamide is highly soluble in water, so it is extracted easily from coffee during brewing. The chemical properties of brewed coffee are influenced by many factors, such as the type of coffee (Arabica, Robusta, or a certain mixture of the two), the degree of roasting, or the amount of water used to make a given amount of coffee, which varies by individual taste. According to some studies the acrylamide content of different coffee beverages ranged from 2 to 25 μg/l [8].

3. Objective

The main objective of my work was to investigate the acrylamide content of different types of capsule coffees by HPLC-MS/MS measurement.

Based on literature data, it was assumed that the acrylamide content of beverages brewed from capsule coffees is higher than the acrylamide content of the ground coffee extracted from the capsules, as it dissolves easily in the water during brewing. The goal was to examine and confirm this with the measurements.

Another objective was to compare different coffee machines. Coffee machines have different parameters (e.g., temperature, pressure, amount of water used), which may affect the amount of acrylamide released from capsule coffees.

Literature data are also available showing how the roasting technology of coffee affects the acrylamide content in the final product. The acrylamide content of so-called light-roasted coffees, roasted for a shorter period of time, is higher than that of dark-roasted coffees, roasted for a longer period of time. This influencing factor was also checked.

Given that decaffeinated coffees are produced by a different technology, some of these types were also examined.

4. Materials and methods

4.1. Chemicals, equipment and instruments used

During my work, analytical grade chemicals, HPLC grade solvent (methanol, acetic acid (anhydrous), n-hexane) and distilled water were used, as well as the following: acrylamide and 10 µg/ml acrylamide-13C3 as internal standard.

In addition to standard laboratory equipment, Biotage ISOLUTE® Multimode 1g/6ml and Biotage ISOLUTE® ENV+500mg/6ml SPE columns were used for sample preparation. For coffee brewing from capsule coffees, the following coffee machines were used: Nespresso Essenza Mini, Krups KP120H31, Tchibo Caffissimo and Martello Smart.

Instrumental analysis of the samples were performed on a Thermo Scientific™ Dionex UltiMate™ 3000 HPLC system with a Phenomenex Kinetex® C18 2.6 µm 100 Å 150x4.6 mm column and a Thermo Scientific™ TSQ Quantis™ triple quadrupole MS detector.

4.2. Sample preparation

Sample preparation and the measurements were carried out as described in standard MSZ EN 16618:2015 Food analysis. Determination of acrylamide in food by liquid chromatography tandem mass spectrometry (LC-ESI-MS/MS).

The samples obtained from commercial sources were caffeinated (25 pcs) and decaffeinated (8 pcs) of capsule coffees from different manufacturers. Measurements were performed on both the ground coffee in the capsules and the brewed coffees. Table 1 shows the sample nos. of the examined coffees and the coffee machines used.

Table 1. Coffees and coffee machines

IBM SPSS Statistics software was used for the statistical evaluation of the results.

5. Results

5.1. Acrylamide content

Acrylamide content measurement results of the coffee samples are summarized in Table 2. Both the results measured in the ground coffees and the results of the corresponding brewed coffees are listed.

Table 2. Measurement results

The results obtained were not in all cases in line with the reference level of 400 μg/kg for roasted coffee set out in Commission Regulation (EU) 2017/2158, as the acrylamide content of some caffeinated samples (nos. 13 and 33) exceeded this level. It is likely that the higher acrylamide level in the case of coffee sample no. 13 was due to the fact that the sample contained Robusta coffee with a higher intensity of acrylamide formation, according to the literature. The result of sample no. 33 can be explained by the fact that is was a hazelnut-flavored mixture. Given that a Robusta variety was added to the Arabica coffee variety, this may have been the reason for the higher results, to which the roasted hazelnut flavor could also have contributed. On average, the acrylamide content of the ground coffees was higher, or in some cases almost identical to the results of the brewed coffees. There were also samples in the case of which the brewed coffees contained more acrylamide than the ground coffees, but most of these values were within the 10% measurement uncertainty.

Based on my statistical (ANOVA) calculations, there was no significant difference between the measurement results of ground and brewed coffees at the 95% confidence level (p > 0.05).

5.2. Effect of brewing on acrylamide content

My objective was to investigate the extent to which the acrylamide content in ground and brewed coffees could vary depending on which coffee machine was used for brewing. Thus, I was looking to answer whether the coffee machines worked with different efficiencies. There was no significant difference between capsule and brewed coffees for any of the coffee machines (p > 0.05).

However, the results showed that, in the case of Martello type capsules, the measured values of both brewed and ground coffees were in a higher range than the results of the other types.

For the Martello type, this range was between 200 and 450 µg/kg (Figure 2), while for the other types (for example, for Nespresso, see Figure 3), typical values were between 100 and 250 µg/kg.

It was found that capsules made for Martello type coffee machines contained ground coffees that typically had a higher acrylamide content. The Martello type capsule coffees tested contained Robusta coffee or a mixture of Robusta and Arabica, which explains the higher acrylamide content, as Robusta-types coffees have higher acrylamide levels than Arabica varieties. One of the Martello type coffee capsules was roasted hazelnut flavored, which also may have contributed to the higher result.

Based on my measurement results, it can be stated that there was no significant difference between the effects of the different coffee machines. However, as the Martello type coffee capsules, on the whole, contains ground coffee with a higher acrylamide concentration compared to the other types, it caused a significant difference between the measurement results of the ground coffees in the capsules.

Figure 2. Measurement results of coffee beverages brewed with a Martello coffee machine
Figure 3. Measurement results of coffee beverages brewed with a Nespresso coffee machine

5.3. Effect of roasting on acrylamide content

It was also examined how different roasting levels affect the amount of the acrylamide formed. In Table 3, coffee samples are grouped according to roasting levels. The ground and brewed samples were marked with separate hues. Figure 4 shows the measured acrylamide amounts according to the different roasting levels. Light roasted samples typically yielded similar of higher results than dark roasted coffees. According to the literature, acrylamide levels of dark roasted coffees are lower than those of light roasted coffees, and this was confirmed by our results.

However, when performing statistical analyses, it was found that there was no significant difference in the amount of acrylamide formed between the results of either ground or brewed coffees at the 95% confidence level (p > 0.05).

The analyses were also performed for the different coffee machines, as the measured values of the coffees brewed with the different machines were typically in different ranges, so this grouping results in a more accurate comparison. However, there was no significant difference between the roasting levels this way either.

Table 3. Acrylamide levels of coffee samples according to roasting levels (see Figure 4. for resolution of hues)
Figure 4. Acrylamide levels of coffee samples according to roasting levels

5.4. Results of caffeinated and decaffeinated coffees

Significantly different techniques are used for the production of decaffeinated coffees, therefore the acrylamide content results of caffeinated and decaffeinated coffees were also compared. The measured values of decaffeinated coffees were in a similar range as the values of caffeinated samples. It was found that there was no significant difference between the different types of samples in terms of acrylamide content.

To confirm this, ANOVA analyses were performed when examining the results of both ground and brewed coffees. At the 95% confidence level, there was no significant difference between the types in either case (p > 0,05).

6. Conclusions

Based on literature data, it was hypothesized that the acrylamide content of the beverages brewed from capsule coffees was higher than the acrylamide content of the ground coffee extracted from the capsules. Based on my measurements, it was found that the acrylamide content of the ground coffees was on average higher than or in some cases similar to the acrylamide levels of the brewed coffees. However, in some cases, brewed coffees did contain more acrylamide. Nevertheless, based on statistical calculations, the difference between the results was not significant. Based on these results, the claims in the literature could not be substantiated unequivocally.

Based on my results, it can be stated that there was no significant difference between the brewed coffees and their ground coffee counterparts in the case of any of the coffee machines in terms of the measured amount of acrylamide. It was found that the acrylamide levels of Robusta type coffees are higher than those of Arabica varieties. The Martello type capsule coffees contained Robusta coffee or a mixture of both, which may explain their higher acrylamide content.

Light roasted samples typically yielded similar or higher acrylamide content results than dark roasted coffees. There was no significant difference in the results of either ground or brewed coffees between the roasting levels.

It was found that there was no significant difference between caffeinated and decaffeinated coffee samples. The acrylamide content of coffee is not significantly affected by the decaffeination processes used.

7. References

[1] Acrylamide. (Hozzáférés: 2020. 01. 27.)

[2] Akrilamid. (Hozzáférés: 2020. 01. 27.)

[3] Löfstedt R. E. (2003): Science Communication and the Swedish Acrylamide ‘‘Alarm’’. Journal of Health Communication, 8 pp. 407–432. DOI

[4] Mottram, D. S., Wedzicha, B. L., Dodson, A. T. (2002): Acrylamide is formed in the Maillard reaction. NATURE, Vol. 419. DOI

[5] Sörgel, F., Weissenbacher, R., Kinzig-Schippers, M., Hofmann, A., Illauer, M., Skott, A., Landersdorfer, C. (2002): Acrylamide: increased concentrations in homemade food and first evidence of its variable absorption from food, variable metabolism and placental and breast milk transfer in humans. S. Karger AG, Basel 0009 3157/02/0486–0267. DOI

[6] Parzefall, W. (2008): Minireview on the toxicity of dietary acrylamide. Food and Chemical Toxicology 46 pp. 1360–1364. DOI

[7] Guenther, H., Anklam, E., Wenzl, T., Stadler, R. H. (2007): Acrylamide in coffee: review of progress in analysis, formation and level reduction. Food Additives & Contaminants, 24 Sup 1, pp. 60-70. DOI

[8] Alves, R. C., Soares, C., Casal, S., Fernandes, J.O., Oliveira, M. Beatriz P.P. (2010): Acrylamide in espresso coffee: influence of species, roast degree and brew length. Food Chemistry 119 pp. 929–934.DOI

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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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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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