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

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.

[1]  W. V. McCarthy,et al.  Discriminant Analysis with Singular Covariance Matrices: Methods and Applications to Spectroscopic Data , 1995 .

[2]  Xu Shu-yan,et al.  Quantitative Analysis Using NIR by Building PLS-BP Model , 2003 .

[3]  David Ward,et al.  Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..

[4]  P. Brown,et al.  NIR and mass spectra classification: Bayesian methods for wavelet-based feature selection , 2005 .

[5]  P. Schellhammer,et al.  Data Reduction Using a Discrete Wavelet Transform in Discriminant Analysis of Very High Dimensionality Data , 2003, Biometrics.

[6]  Hai-bin Qu,et al.  [A new approach to the fast measurement of content of amino acids in Cordyceps sinensis by ANN-NIR]. , 2004, Guang pu xue yu guang pu fen xi = Guang pu.

[7]  M. Almond,et al.  Book reviewPractical NIR spectroscopy: By B. G. Osborne, T. Fearn & P. H. Hindle. Longmans, UK, 1993. 227pp. ISBN 0582-099463. Price: £65.00 , 1994 .

[8]  H. Utku,et al.  Application of the feature selection method to discriminate digitized wheat varieties. , 2000 .

[9]  G. Bisztray,et al.  Distinguishing melon genotypes using NIR spectroscopy , 2004 .

[10]  Shuijuan Feng,et al.  Study on lossless discrimination of varieties of yogurt using the Visible/NIR-spectroscopy , 2006 .

[11]  J. M. González-Sáiz,et al.  An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS , 2004 .

[12]  A. Formaggio,et al.  Discrimination of sugarcane varieties in Southeastern Brazil with EO-1 Hyperion data , 2005 .