A Novel Approach to Pattern Recognition Based on PCA-ANN in Spectroscopy

  • Xiaoli Li
  • Yong He
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


Pattern recognition problems that involve functional predictors has developed, specifically for spectral data. The classification of three peach varieties based on near infrared spectra was researched in the practical context. Principal component analysis (PCA) and artificial neural networks (ANN) were used for pattern recognition in this research. PCA is a very effective data mining way; it is applied to enhance species features and reduce data dimensionality. ANN with back propagation algorithm was used for the data compression tasks as well as class discrimination tasks. The first 9 principal components computed by PCA were applied as inputs to a back propagation neural network with one hidden layer. This model was used to predict the varieties of 15 unknown samples. The recognition rate of the model for the unknown sample was 100%. So this paper could offer an effective pattern recognition way.


Artificial Neural Network Linear Discriminant Analysis Back Propagation Neural Network Near Infrared Spectroscopy Back Propagation Algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaoli Li
    • 1
  • Yong He
    • 1
  1. 1.College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina

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