Application of Principal Component Analysis-Artificial Neural Network in Near Infrared Spectroscopy for Non-destructive Determination of Coriolus Versicolor

Abstract

We have applied principal component analysis-artificial neural network (PCA-ANN) in near infrared (NIR) spectroscopy to synchronous and rapid determining the contents of polysaccharide and protein in the Coriolus versicolor Powders. Back-Propagation (BP) Networks which adopt Levenberg-Marquardt training algorithm have been developed. Via analyzing the NIR spectra matrix by principal component analysis (PCA) method, we have obtained the principal components (PC) scores. The original NIR spectra and PC scores were respectively used as input data. These developed BP Networks have been optimized by selecting suitable topologic parameters and the best numbers of training. Compare with original NIR spectra, using the PC scores as input data, the capabilities of BP networks were much better. Using these optimized BP Networks for predicting the contents of polysaccharide and protein in prediction set, the root mean square error of prediction (RMSEP) are 0.0141 and 0.0138. These results are so satisfied and NIR spectroscopy technology is convenient, rapid, no pretreatment and no pollution that this method could be popularized in the in situ measurement and the on-line quality control for fermentation.