Food and Fermentation Industries >

2013 , Vol. 39 >Issue 03: 48 - 51

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.2013.03.025

Food and Fermentation Industries

Estimation of beer sensory evaluation based on principal component analysis and neural network

  • Zhong Chen ,
  • Huang Yi-wen ,
  • Jia Shiru ,
  • Dong Jian-jun ,
  • Hao Jun-guang ,
  • Li Qing-liang
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Online published: 2013-03-25

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Abstract

The method integrating principal component analysis(PCA) with back propagation(BP) neural networks was applied to predict the beer sensory evaluation.A total of 11 physicochemical indexes was extracted by PCA to eliminate linear relevance and then used as input.Sensory evaluation scores were used as output.BP neural network was established to model the beer sensory evaluation.Finally,sensory evaluation scores of 50 beers were predicted by this model.The experimental results showed that the maximum predictive relative error was 2.68%,suggesting that the methods integrating PCA with BP networks could precisely predict the beer sensory evaluation.

Key words： flavor component; principal component analysis; back propagation neural network; beer sensory evaluation

Cite this article

Zhong Chen , Huang Yi-wen , Jia Shiru , Dong Jian-jun , Hao Jun-guang , Li Qing-liang . Estimation of beer sensory evaluation based on principal component analysis and neural network[J]. Food and Fermentation Industries, 2013 , 39(03) : 48 -51 . DOI: 10.13995/j.cnki.11-1802/ts.2013.03.025

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