| 光谱学与光谱分析 |
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| Application of DPLS-Based LDA in Corn Qualitative Near Infrared Spectroscopy Analysis |
| QIN Hong, WANG Hui-rong, LI Wei-jun*, JIN Xiao-xian |
| Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China |
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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.
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Received: 2010-09-21
Accepted: 2010-12-18
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Corresponding Authors:
LI Wei-jun
E-mail: wjli@semi.ac.cn
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