光谱学与光谱分析 |
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Recognition of Corn Seeds Based on Pattern Recognition and Near Infrared Spectroscopy Technology |
LIU Tian-ling1, SU Qi-ya1, SUN Qun2, YANG Li-ming1* |
1. College of Science, China Agricultural University, Beijing 100083, China2. College of Agriculture and Biotechnology, China Agricultural University, Beijing 100193, China |
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Abstract Pattern recognition technology and data mining methods have become a hot topic in chemometrics. Near infrared (NIR) spectroscopic analysis has been widely used in spectrum signal processing and modeling since it has advantages of quickness, simplicity and nondestructiveness. Based on five different methods of pattern recognition, namely the locally linear embedding (LLE), wavelet transform (WT), principal component analysis (PCA), partial least squares (PLS) and support vector machine (SVM), the pattern recognition system for corn seeds was proposed using NIR technology, and applied to classification of 108 hybrid samples and 178 female samples for corn seeds. Firstly, we get rid of noise or reduce the dimension using LLE, WT, PCA, PLS, and then use SVM to identify two-class samples. In the meantime, 1-norm SVM is the method of direct classification and identification. Experimental results of three different spectral regions show that the performances of three methods: PCA+SVM, LLE+SVM, PLS+SVM are superior to WT+SVM and 1-norm SVM methods, and obtain a high classification accuracy, which indicates the feasibility and effectiveness of the proposed methods. Moreover, this investigation provides the theoretical support and practical method for recognition of corn seeds utilizing near infrared spectral data.
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Received: 2011-10-30
Accepted: 2012-02-02
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Corresponding Authors:
YANG Li-ming
E-mail: cauylm@126.com
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[1] BAI Ou,HUANG Rui-dong(白 鸥,黄瑞冬). Journal of Maize Sciences(玉米科学),2007,15(3):59. [2] ZHOU Shu-ping,CHENG Gui-min,Ll Wei-hong,et al(周淑平,程贵敏,李卫红,等). Guizhou Agricultural Sciences(贵州农业科学),2007,35(1):28. [3] Dupuy N,Galtier O,Ollivier D. Analytica Chimica Acta,2010,666(1):23. [4] Juan Antonio Fernandez Pierna,Ouissam Abbas,Vincent Baeten. Analytica Chimica Acta,2009,642(1):89. [5] Lucia onofrejova,Marta Farkova,Jan Preisler. Analytica Chimica Acta,2009,638(2):191. [6] Pascal C,Serge W,Michel U. Analytica Chimica Acta,2007,591(2):219. [7] Debska B,Guzowska-Swider B. Analytica Chimica Acta,2011,705(1):283. [8] Fernández Pierna J A, Lecler B, Conzen J P. Analytica Chimica Acta,2011,705(1):30. [9] Jan Luts, Fabian Ojeda, Raf Van de Plas. Analytica Chimica Acta,2010,665(2):129. [10] Ustun B,Melssen W J,Oudenhuijzen M,et al. Analytica Chimica Acta,2005,544(1):292. [11] Roweis S T,Saul L K. Science,2000,290:2323. [12] Saul L T,Roweis S T. Journal of Machine Learning Research,2004,5(3):119. [13] WANG Hui-qin(王慧琴). The Wavelet Analysis and its Application(小波分析与应用). Beijing:Beijing University of Posts and Telecommunications Press(北京:北京邮电大学出版社),2011. 1. [14] ZHANG Jing-yuan,ZHANG Bing,JIANG Xing-zhou(张静远,张 冰,蒋兴舟). Signal Processing(信号处理),2000,16(2):156. [15] HUANG Yan-yan,ZHU Li-wei,LI Jun-hui,et al(黄艳艳,朱丽伟,李军会,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2011,31(3):661.
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