title: [Application of DPLS-based LDA in corn qualitative near infrared spectroscopy analysis].
abstract: 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.
authors.affiliation: Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China. qinh@semi.ac.cn
authors.forename: Hong
authors.initials: H
authors.lastname: Qin
authors.forename: Hui-rong
authors.initials: HR
authors.lastname: Wang
authors.forename: Wei-jun
authors.initials: WJ
authors.lastname: Li
authors.forename: Xiao-xian
authors.initials: XX
authors.lastname: Jin
mesh_terms: D016002:Discriminant Analysis; D016018:Least-Squares Analysis; D019265:Spectroscopy, Near-Infrared; D003313:Zea mays / Q000145:classification*