CONSUMER PREFERENCE RESEARCH IN FORMING RATIONAL COMPONENT OF REGIONAL BRANDS IN THE MEAT PRODUCT MARKET

The use of α-lactoglobulin hydrolysate produced with the use of Flavorpro 750MDP and Promod 439L enzyme preparations to obtain fermented milk drinks having reduced residual antigenicity has been suggested. It is necessary to provide normalized physical-chemical and acceptable organoleptic characteri...

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Main Authors: Mel'nikova E.I., Ponomareva N.V., Bogdanova E.V.
Format: Article
Language:Russian
Published: Kemerovo State University 2017-03-01
Series:Техника и технология пищевых производств
Subjects:
Online Access:http://fptt.ru/stories/archive/44/23.pdf
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spelling doaj-b3c979f7e7e244e6b648530be74415f32020-11-25T02:00:18ZrusKemerovo State UniversityТехника и технология пищевых производств2074-94142313-17482017-03-0144115215810.21179/2074-9414-2017-1-152-158CONSUMER PREFERENCE RESEARCH IN FORMING RATIONAL COMPONENT OF REGIONAL BRANDS IN THE MEAT PRODUCT MARKETMel'nikova E.I.0Ponomareva N.V.1Bogdanova E.V.2Voronezh State University of Engineering TechnologiesVoronezh State University of Engineering TechnologiesVoronezh State University of Engineering TechnologiesThe use of α-lactoglobulin hydrolysate produced with the use of Flavorpro 750MDP and Promod 439L enzyme preparations to obtain fermented milk drinks having reduced residual antigenicity has been suggested. It is necessary to provide normalized physical-chemical and acceptable organoleptic characteristics of resulting fermented milk drink. The purpose of the research is to study the possibility of applying the method of artificial neural networks for prediction of properties, quality indices and safety factors of normalized dairy mixes used to obtain low-allergenic fermented milk drinks. Organoleptic characteristics, physical and chemical properties of the normalized mixes have been studied with sensorymetric method. The method of evaluation of quality indices of normalized mixes and fermented milk drinks has been adapted using the method of artificial neural networks. Three-layer neural network with 6 neurons in the input layer, 12 neurons in the inner layer and 4 neurons in the output layer according to the number of output parameters has been used. The algorithm of back-propagation errors has been applied for training the network. The research results confirm that the obtained neural network predicts the main characteristics of normalized mixes with α-lactoglobulin hydrolysate almost accurately; the relative error does not exceed 2.6% when predicting α-lactoglobulin content, 3.9% when predicting residual antigenicity and 3.1% when predicting titratable acidity and organoleptic characteristics. This method is applicable for assessing the quality of finished goods and can replace the routine methods of analysis in force at the enterprises of dairy industry.http://fptt.ru/stories/archive/44/23.pdfartificial neural networksquality index evaluationlow-allergy fermented milk drinks
collection DOAJ
language Russian
format Article
sources DOAJ
author Mel'nikova E.I.
Ponomareva N.V.
Bogdanova E.V.
spellingShingle Mel'nikova E.I.
Ponomareva N.V.
Bogdanova E.V.
CONSUMER PREFERENCE RESEARCH IN FORMING RATIONAL COMPONENT OF REGIONAL BRANDS IN THE MEAT PRODUCT MARKET
Техника и технология пищевых производств
artificial neural networks
quality index evaluation
low-allergy fermented milk drinks
author_facet Mel'nikova E.I.
Ponomareva N.V.
Bogdanova E.V.
author_sort Mel'nikova E.I.
title CONSUMER PREFERENCE RESEARCH IN FORMING RATIONAL COMPONENT OF REGIONAL BRANDS IN THE MEAT PRODUCT MARKET
title_short CONSUMER PREFERENCE RESEARCH IN FORMING RATIONAL COMPONENT OF REGIONAL BRANDS IN THE MEAT PRODUCT MARKET
title_full CONSUMER PREFERENCE RESEARCH IN FORMING RATIONAL COMPONENT OF REGIONAL BRANDS IN THE MEAT PRODUCT MARKET
title_fullStr CONSUMER PREFERENCE RESEARCH IN FORMING RATIONAL COMPONENT OF REGIONAL BRANDS IN THE MEAT PRODUCT MARKET
title_full_unstemmed CONSUMER PREFERENCE RESEARCH IN FORMING RATIONAL COMPONENT OF REGIONAL BRANDS IN THE MEAT PRODUCT MARKET
title_sort consumer preference research in forming rational component of regional brands in the meat product market
publisher Kemerovo State University
series Техника и технология пищевых производств
issn 2074-9414
2313-1748
publishDate 2017-03-01
description The use of α-lactoglobulin hydrolysate produced with the use of Flavorpro 750MDP and Promod 439L enzyme preparations to obtain fermented milk drinks having reduced residual antigenicity has been suggested. It is necessary to provide normalized physical-chemical and acceptable organoleptic characteristics of resulting fermented milk drink. The purpose of the research is to study the possibility of applying the method of artificial neural networks for prediction of properties, quality indices and safety factors of normalized dairy mixes used to obtain low-allergenic fermented milk drinks. Organoleptic characteristics, physical and chemical properties of the normalized mixes have been studied with sensorymetric method. The method of evaluation of quality indices of normalized mixes and fermented milk drinks has been adapted using the method of artificial neural networks. Three-layer neural network with 6 neurons in the input layer, 12 neurons in the inner layer and 4 neurons in the output layer according to the number of output parameters has been used. The algorithm of back-propagation errors has been applied for training the network. The research results confirm that the obtained neural network predicts the main characteristics of normalized mixes with α-lactoglobulin hydrolysate almost accurately; the relative error does not exceed 2.6% when predicting α-lactoglobulin content, 3.9% when predicting residual antigenicity and 3.1% when predicting titratable acidity and organoleptic characteristics. This method is applicable for assessing the quality of finished goods and can replace the routine methods of analysis in force at the enterprises of dairy industry.
topic artificial neural networks
quality index evaluation
low-allergy fermented milk drinks
url http://fptt.ru/stories/archive/44/23.pdf
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