Artificial Neural Networks for Predicting Food Antiradical Potential

Applied Sciences

Vladimir Nosov¹  Marina Nikitina²  Victor Gorbachev³  Daria Velina³  Galina Korneva³  Igor Sokolov³  Igor Nikitin³  Sherzodkhon Mutallibzoda³  Anna Terekhova³  Elena Artemova⁴  Bella Khashir⁵  Svetlana Dimitrieva⁶ 

[1] Agrarian Technological Institute, Peoples’ Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia;Centre for Economic and Analytical Research and Information Technology, V.M. Gorbatov Federal Research Center for Food Systems of RAS, 26 Talalikhina Street, 109316 Moscow, Russia;Department of Biotechnology of Food Products from Plant and Animal Raw Materials, K.G. Razumovsky Moscow State University of Technologies and Management (The First Cossack University), 73 Zemlyanoy Val, 109004 Moscow, Russia;Department of Economic Theory, Kuban State Agrarian University, 213 Kalinina Street, 350044 Krasnodar, Russia;Department of Economics and Finance, Kuban State Technological University, 2 Moskovskaya Street, 350072 Krasnodar, Russia;S.I. Vavilov Department of Luminescence, P.N. Lebedev Physical Institute of the Russian Academy of Sciences, 53 Leninsky Prospekt, 119333 Moscow, Russia;

关键词: antiradical activity;    DPPH (2,2-diphenyl-1-picrylhydrazyl);    bread;    confectionery;    milk;    eggs;   

DOI  :  10.3390/app12126290

来源: DOAJ

【 摘 要 】

Using an artificial neural network (ANN), the values of the antiradical potential of 1315 items of food and agricultural raw materials were calculated. We used an ANN with the structure of a “multilayer perceptron” (MLP) and with the hyberbolic tangent (Tanh) as an activation function. Values reported in the United States Food and Nutrient Database for Dietary Studies (FNDDS) were taken as input to the analysis. When training the ANN, 60 parameters were used, such as the content of plastic substances, food calories, the amount of mineral components, vitamins, the composition of fatty acids and additional substances presented in this database. The analysis revealed correlations, namely, a direct relationship between the value of the antiradical potential (ARP) of food and the concentration of dietary fiber (r = 0.539) and a negative correlation between the value of ARP and the total calorie content of food (r = −0.432) at a significance level of p < 0.001 for both values. The average ARP value for 10 product groups within the 95% CI (confidence interval) was ≈23–28 equivalents (in terms of ascorbic acid) per 1 g of dry matter. The study also evaluated the range of average values of the daily recommended intake of food components (according to Food and Agriculture Organization—FAO, World Health Organization—WHO, Russia and the USA), which within the 95% CI, amounted to 23.41–28.98 equivalents per 1 g of dry weight. Based on the results of the study, it was found that the predicted ARP values depend not only on the type of raw materials and the method of their processing, but also on a number of other environmental and technological factors that make it difficult to obtain accurate values.

【 授权许可】

Unknown