BP neural networks combined with PLS applied to pattern recognition of Vis/NIRs

Authors:
Di Wu;Yong He;Yongni Shao;Shuijuan Feng
Affiliations:
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China;College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China;College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China;College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Improving recognition and generalization capability of back-propagation NN using a self-organized network inspired by immune algorithm (SONIA)

Applied Soft Computing

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Abstract

Vis/NIRs technique can be used in non-destructive measurement of the material internal quality in many fields. In this study, a mixed algorithm combined with back-propagation neural networks (BPNNs) and partial least squares (PLS) method was applied in the predicting the acidity of yogurt. The reflectance of optimal wavebands selected by PLS process were set as input neurons of BPNNs to establish the prediction model. By training the 130 yogurt samples in the BPNNs of topological structure 19:11:1, the acidity of the remaining 25 samples were predicted. The correlation between the measured and predicted values shows an excellent prediction performance with the value of 0.97, higher than the result (0.916) obtained only by PLS. Thus, it is concluded that the algorithm construct by BPNNs combined with partial least square applied to pattern recognition is an available alternative for pattern recognition based on Vis/NIRs.