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Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP

Self‐made milk knots in Xinjiang Kazakh ethnic group were used as material to establish the quality assessment system of flavor quality. The fuzzy analytic hierarchy process based on the optimal consistency matrix was used to evaluate the quality of the samples qualitatively and quantitatively. Its...

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Detalles Bibliográficos
Autores principales: Wei, Zhisheng, Ma, Xueping, Zhan, Ping, Tian, Honglei, Li, Kaixuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7174216/
https://www.ncbi.nlm.nih.gov/pubmed/32328275
http://dx.doi.org/10.1002/fsn3.1501
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author Wei, Zhisheng
Ma, Xueping
Zhan, Ping
Tian, Honglei
Li, Kaixuan
author_facet Wei, Zhisheng
Ma, Xueping
Zhan, Ping
Tian, Honglei
Li, Kaixuan
author_sort Wei, Zhisheng
collection PubMed
description Self‐made milk knots in Xinjiang Kazakh ethnic group were used as material to establish the quality assessment system of flavor quality. The fuzzy analytic hierarchy process based on the optimal consistency matrix was used to evaluate the quality of the samples qualitatively and quantitatively. Its result is consistent with the cluster analysis of the SOM neural network. The results showed that the milk knot samples of Altay had differences with the milk knot samples of Yili. The comprehensive evaluation system is feasible and can evaluate the quality of milk knot samples by flavor characteristics. This can provide a reference for further research on the origin of differences between two types of milk knot samples.
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spelling pubmed-71742162020-04-23 Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP Wei, Zhisheng Ma, Xueping Zhan, Ping Tian, Honglei Li, Kaixuan Food Sci Nutr Original Research Self‐made milk knots in Xinjiang Kazakh ethnic group were used as material to establish the quality assessment system of flavor quality. The fuzzy analytic hierarchy process based on the optimal consistency matrix was used to evaluate the quality of the samples qualitatively and quantitatively. Its result is consistent with the cluster analysis of the SOM neural network. The results showed that the milk knot samples of Altay had differences with the milk knot samples of Yili. The comprehensive evaluation system is feasible and can evaluate the quality of milk knot samples by flavor characteristics. This can provide a reference for further research on the origin of differences between two types of milk knot samples. John Wiley and Sons Inc. 2020-03-21 /pmc/articles/PMC7174216/ /pubmed/32328275 http://dx.doi.org/10.1002/fsn3.1501 Text en © 2020 The Authors. Food Science & Nutrition published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Wei, Zhisheng
Ma, Xueping
Zhan, Ping
Tian, Honglei
Li, Kaixuan
Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP
title Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP
title_full Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP
title_fullStr Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP
title_full_unstemmed Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP
title_short Flavor quality evaluation system of Xinjiang milk knots by using SOM neural network and the fuzzy AHP
title_sort flavor quality evaluation system of xinjiang milk knots by using som neural network and the fuzzy ahp
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7174216/
https://www.ncbi.nlm.nih.gov/pubmed/32328275
http://dx.doi.org/10.1002/fsn3.1501
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