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.
Key words:Near infrared spectroscopy;Linear discriminant analysis;Discriminant partial least square;Qualitative analysis;Corn
覃 鸿,王徽蓉,李卫军*,金小贤. 基于DPLS特征提取的LDA方法在玉米近红外光谱定性分析中的应用 [J]. 光谱学与光谱分析, 2011, 31(07): 1777-1781.
QIN Hong, WANG Hui-rong, LI Wei-jun*, JIN Xiao-xian. Application of DPLS-Based LDA in Corn Qualitative Near Infrared Spectroscopy Analysis . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31(07): 1777-1781.
[1] LUO Cheng-gang, HA Jun-li, ZHOU Yi-he, et al(罗成刚, 哈君利, 周义和, 等). Acta Tabacaria Sinica(中国烟草学报), 1999, 5(3): 42. [2] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄,赵龙莲,韩东海,等). Foundation and Application of Near-Infrared Spectroscopy Analysis(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京:中国轻工业出版社), 2005. [3] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍,袁洪福,徐广通,等). Modern Near Infrared Spectroscopy Analytical Technology (Second Edition)(现代近红外光谱分析技术,第2版). Beijing: China Petrochemical Press(北京:中国石化出版社), 2007. [4] CHEN Quan-sheng, ZHAO Jie-wen, ZHANG Hai-dong, et al(陈全胜,赵杰文, 张海东, 等). Food Science(食品科学), 2006, 27(4):186. [5] WU Wen-jin, WANG Hong-wu, CHEN Shao-jiang, et al(邬文锦,王红武, 陈绍江, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010, 30(5): 1248. [6] ZHAI Ya-feng, SU Qian, WU Wen-jin, et al(翟亚锋,苏 谦, 邬文锦, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2010, 30(4): 924. [7] Duda Richard O, Hart Peter E, et al. Pattern Classification(模式分类). Beijing: China Machine Press(北京:机械工业出版社), 2003. [8] Zhao W, Chellappa R, et al. Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998. 336. [9] Kawatani T, Shimizu H. Proceedings of Fourteenth International Conference on Pattern Recognition, 1998, 2: 1301. [10] Fan Bin, Lei Zhen, et al. Proceedings of 8th IEEE International Conference on Automatic Face and Gesture Recognition, 2008. 1. [11] LU Wan-zhen, YUAN Hong-fu, CHU Xiao-li(陆婉珍,袁洪福, 褚小立). Near Infrared Spectrometer(近红外光谱仪器). Beijing: Chemical Industry Press (北京:化学工业出版社), 2010. [12] Svante Wold, Michael Sjostroma, et al. Chemometrics and Intelligent Laboratory Systems, 2001, 58: 109.