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BP Neural Networks Combined with PLS Applied to Pattern Recognition of Vis/NIRs

  • Di Wu
  • Yong He
  • Yongni Shao
  • Shuijuan Feng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

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.

Keywords

Partial Little Square Principal Component Regression Partial Little Square Analysis Yogurt Sample Multiplicative Scatter Correction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Di Wu
    • 1
  • Yong He
    • 1
  • Yongni Shao
    • 1
  • Shuijuan Feng
    • 1
  1. 1.College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina

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