Application of DPLS-Based LDA in Corn Qualitative Near Infrared Spectroscopy Analysis

NIR technology is a rapid,nondestructive and user-friendly method ideally suited for Qualitative analysis.In this paper the authors present the use of discriminant partial least Squares(DPLS)-based linear discriminant analysis(LDA) in corn qualitative near infrared spectroscopy analysis.Firstly,a training set including 30 corn varieties(each variety has 20 samples) was used to build the DPLS regression model,and 28 principal components(DPLS-PCs) were obtained from original spectrum.Secondly,the DPLS-PCs scores of the training set were extracted as DPLS features.Thirdly,LDA was applied to the DPLS features,determining 26 principal components(LDA-PCs).A test sample was first projected onto the DPLS-PCs and then onto the LDA-PCs,and finally 26 DPLS+LDA features were obtained.The recognition results were obtained by minimum distance classifier.DPLS+LDA method achieved 96.18% recognition rate,while traditional DPLS regression method and DPLS feature extraction method only achieved 85.38% and 95.76% recognition rate respectively.The experiment results indicated that DPLS+LDA method is with better generalization ability compared with traditional DPLS regression method and NIRS analysis by DPLS+LDA method is an efficient way to discriminate corn species.