Author: Pál Molnár
Multivariate statistical methods that can be run on computers, can can also be used for the classification of the sensory properties of foods. Sensory testing categories of „excellent”, „good”, „average”, „suitable” and „unsuitable” that are usually determined via a traditional classification procedure, can also be created by the normalization and statistical processing of product testing data with the help of a software. In this paper, a rough overview of the classification of bakery products, using numerical values obtained during sensory testing of product shape, crust, smell, taste and crumb, and weighting factors is given. Traditional classification calculations and computerized evaluation based on shape recognition showed good agreement in the case of the products tested. The programs developed – in case of a suitable data population – can be applied to other products as well. With further refinements, such as homogeneity analysis, shape recognition methods can help greatly the further development of not only sensory, but complex food testing.
In the area of food classification, the complexity of the concept of quality, its dynamic change and relativity raise several problems, the solution of which, as well as decision making, inescapably need the application of the tools of system analysis. Type creation and classification, detection of similarities and differences, recognition and numerical quantification of the importance and the role of individual criteria and properties, establishing the connection between important characteristics is a very difficult and complex task. These are greatly aided by mathematical methods under the heading similarity theory or shape recognition.
Similarity theory generally deals with the understanding and expression, with the help of scientific tools, of the similarities and differences of large numbers of abstract or real shapes that have identical criteria or properties, but different numerical characteristics and dimensions: objects, elements, systems, sometimes the results or states originating from the actions of living organisms etc., in order to recognize properly the importance, role and significance of the individual criteria and properties. With the help of the knowledge obtained through the continuation of the series of analyses published in the paper of Molnár, Liszonyi and Őrsi , we performed preferring, organization, qualification, classification or ranking, which provided us with new information to make judging and decision-making more exact, reliable and efficient.
According to Martens and Martens, the basic idea of scientific methods of shape recognition is that – based on similarity theory – measurable properties, criteria of the objects to be recognized are subjected to computerized analysis, selecting, grouping relevant ones which are characteristic of the individuals to be recognized . The methods applied are the following: cluster analysis, discriminant analysis or a combination of these. Its highest levels are reached by learning algorithms, whose recognition reliability increases with the number of data processed.
According to Lásztity and Őrsi, one of the cardinal questions of food qualification is quantifying the position of product quality on a given standard scale . Correct qualification can be very helpful in judging the quality level of a product, product group or industrial sector, or the success food research aimed at product development. Lately, conceptual development of an index expressing food quality, a quality indicator has been on the agenda. According to Molnár, when updating the qualification procedure, shape recognition methods can be utilized especially for solving the following problems .