Multi-criteria food products identification by fuzzy logic methods

Abstract

The paper deals with the theory of fuzzy sets as applied to food industry products. The fuzzy indicator function is shown as a criterion for determining the properties of the product. We compared the approach of fuzzy and probabilistic classifiers, their fundamental differences and areas of applicability. As an example, a linear fuzzy classifier of the product according to one-dimensional criterion was given and an algorithm for its origination as well as approximation is considered, the latter being sufficient for the food industry for the most common case with one truth interval where the indicator function takes the form of a trapezoid. The results section contains exhaustive, reproducible, sequentially stated examples of fuzzy logic methods application for properties authentication and group affiliation of food products. Exemplified by measurements of the criterion with an error, we gave recommendations for determining the boundaries of interval identification for foods of mixed composition. Harrington’s desirability function is considered as a suitable indicator function of determining deterioration rate of a food product over time. Applying the fuzzy logic framework, identification areas of a product for the safety index by the time interval in which the counterparty selling this product should send it for processing, hedging their possible risks connected with the expiry date expand. In the example of multi-criteria evaluation of a food product consumer attractiveness, Harrington’s desirability function, acting as a quality function, was combined with Weibull probability density function, accounting for the product’s taste properties. The convex combination of these two criteria was assumed to be the decision-making function of the seller, by which identification areas of the food product are established.

Production of a mixed fruit juice powder using mixture analysis and a fuzzy model

“…Involving the weightage of attributes in the final decision of sensory evaluation is important for precise sensory evaluation. Fuzzy logic methods have already been applied to insert the weightage of sensory attributes (Oganesyants et al, 2020;Dhar et al, 2021). The fuzzy logic method (similarity analysis) has also been applied to rank the attributes (Kaushik et al, 2015;Sugumar and Guha, 2022).…”

“…Sugumar and Guha (2022) also applied the method to analyse a soup produced from European black nightshade (Solanum nigrum L.). The basis of the method is the production of polynomial models from sensory data, and the calculation of a single sensory score from different attributes (Oganesyants et al, 2020;Dhar et al, 2021). In addition to the fuzzy algorithm, the fuzzy toolbox has also been attended as a powerful method for ranking and scoring food samples (Zare and Ghazali, 2015).…”

Determining food safety in canned food using fuzzy logic based on sulphur dioxide, benzoic acid and sorbic acid concentration

“…The FL provided an insight into variation in consumer acceptability with respect to flavor, color, taste, and mouthfeel of raw apple juice under the influence of the essential oil of betel leaf [ 34 ]. Besides, FL has been said to be effective in making decisions about the quality and safety of food products [ 35 ]. There is also a study showing that the FL is able to assess the quality of fish based on biogenic amine contents at different temperatures and storage times [ 36 ].…”

Principles of formation of modular technologies of enteral nutrition products

“…Цифровое профилирование позволяет приблизить профиль проектируемого продукта к некоторой контрольной матрице, однако имеет ограниченную применимость в случае продуктов ЭП. Классическое цифровое профилирование оперирует комбинаторикой химического состава, но практически не учитывает формируемые этим составом физические и органолептические свойства (реология, кислотность, окислительно-восстановительный потенциал), а также физиологические особенности состояния потенциального потребителя(Хуршудян & Рябова, 2021; Oganesyantset al, 2018a;Oganesyants et al, 2018b).В качестве эффективного пути нивелирования указанных недостатков целесообразным видится формирование системы расширенного цифрового профилирования, представляющеющей, по сути, комплексное применение принципов цифрового профилирования и пищевой комбинаторики посредством введения дополнительного элемента -цифрового профиля состояния пациента (D D ) и органичным дополнением от него отдельных функциональных зависимостей элементов цифрового профиля продукта (D p ). В этом случае формат цифрового профиля продукта примет вид D p (A, f Q , f O , I ) , где A -базовая матрица или состав продукта, f Q -матрица физико-химических параметров, представленная, по сути, матрицей многопараметрических функций от компонентного состава, f O -матрица органолептических показателей, которая также, как и предыдущая, включается в виде ма-трицы функции от состава, но кроме того и от физико-химических показателей, I -кортеж метаданных.…”