The Neural network development of food formulations and evaluation of the quality characteristics of finished products
https://doi.org/10.37493/2307-910X.2024.1.7
Abstract
Using statistical methods of visualization of experimental data, the use of artificial intelligence for the development of optimal food formulations in terms of qualitative characteristics is justified. Using the example of sausage products, a method for identifying the prescription composition of food products has been developed for comparison with the indicators of technical documentation. The assessment of the adequacy of the chemical and amino acid compositions of the formulations of the technical documentation confirmed the effectiveness of the developed methodology.
About the Authors
V. V. Sadovoy
Belgorod University of Cooperation, Economics and Law, Stavropol Institute of Cooperation (branch)
Russian Federation
Russian Federation
Vladimir V. Sadovoy – Dr. Sci. (Techn.), Professor of the Department of Commodity Science and Public Catering Technology
Stavropol
+79188639013
T. V. Shchedrina
North-Caucasus Federal University, Pyatigorsk Institute (branch)
Russian Federation
Russian Federation
Tatiana V. Shchedrina – Cand. Sci. (Techn.), Associate Professor of the Department of Food Technology and Commodity Science
Pyatigorsk
+79283730813
А. S. Khamitsaeva
Gorsky State Agrarian University
Russian Federation
Russian Federation
Alla S. Khamitsaeva – Dr. Sci. (Techn.), Professor of the Department of Food Technology
Vladikavkaz
+79034833663
I. А. Trubina
Stavropol State Agrarian University
Russian Federation
Russian Federation
Irina A. Trubina – Cand. Sci. (Techn.), Associate Professor, Department of Production and Processing of Agricultural Products
Stavropol
+79624419174
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Review
For citations:
Sadovoy V.V., Shchedrina T.V., Khamitsaeva А.S., Trubina I.А. The Neural network development of food formulations and evaluation of the quality characteristics of finished products. Modern Science and Innovations. 2024;(1):71-79. https://doi.org/10.37493/2307-910X.2024.1.7
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