| 光谱学与光谱分析 |
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| Research on the Parameter Drift Problem of Near Infrared Spectra Based Corn Variety Discrimination Technology |
| ZHANG Li-ping1, 2, LI Wei-jun2*, WANG Ping1, AN Dong3 |
1. College of Information and Control Engineering, China University of Petroleum(Huadong), Qingdao 266580, China
2. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China |
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Abstract Aiming to differentiate 13 varieties of corn, present paper proposes an effective approach to solving the parameter drift problem of spectrum instruments. Remarkable drift has been found among the inter-day data when using the identical spectrum instrument to acquire sample data at different times, modeling with the intra-day data, and testing with the rest. The correct recognition rate is reduced to only 7.69% in the condition of severe drift. To tackle this problem, this paper proposes a supervised feature-based inter-day combination modeling approach, at first, the representative sample data acquired at multiple times will be selected to make up the modeling set, and then the PLS+LDA algorithm will be applied to extract the feature of varieties which is independent on instrument parameter drift, and finally BPR will be used to identify the varieties. The experiment results indicate that this approach is effective to rectify the data drift at different times, can bring higher recognition rate, and also shows its stability in practice.
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Received: 2012-04-25
Accepted: 2012-07-25
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Corresponding Authors:
LI Wei-jun
E-mail: wjli@semi.ac.cn
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[1] HE Yong, LI Xiao-li(何 勇，李晓丽). J. Infrared Millim. Waves(红外与毫米波学报), 2006, 25(3): 192.
[2] CAO Fang, WU Di, HE Yong, et al(曹 芳，吴 迪，何 勇，等). Acta Optica Sinica(光学学报), 2009, 29(2): 537.
[3] LIANG Liang, LIU Zhi-xiao, YANG Min-hua, et al(梁 亮，刘志宵，杨敏华，等). J. Infrared Millim. Waves(红外与毫米波学报), 2009, 28(5): 353.
[4] 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.
[5] 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.
[6] QIN Hong,WANG Hui-rong, LI Wei-jun, et al(覃 鸿，王徽蓉，李卫军，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(7): 1777.
[7] Hu R W, Xia J F. IEEE Electric Information and Control Engineering(ICEICE), 2011, 5(27): 3062.
[8] WANG Ju-xiang, LI Hua, XING Zhi-na, et al(王菊香，李 华，邢志娜，等). Chinese Journal of Analytical Chemistry(分析化学), 2011, 39(6): 846.
[9] Svante Wold, Michael Sjostroma, et al. Chemometrics and Intelligent Laboratory Systems, 2001, 58: 109.
[10] ZHU Er-yi, YANG Peng-yuan(朱尔一，杨芃原). Chemometrics Technology and Application(化学计量学技术及应用). Beijing: Science Press(北京：科学出版社), 2001.
[11] Duda R O, Hart P E, Stork D G. Pattern Classification(模式分类). Translated by LI Hong-dong, YAO Tian-xiang, et al(李宏东, 姚天翔, 等译). Beijing: China Machine Press(北京：机械工业出版社), 2003.
[12] CHEN Fu-bing, ZHANG Sheng-liang, GAO Xiu-mei(陈伏兵，张生亮，高秀梅，等). Journal of Image and Graphics(中国图象图形学报), 2005, 10(8): 984.
[13] Fan Bin, Lei Zhen, et al. Proceedings of 8th IEEE International Conference on Automatic Face & Gesture Recognition, 2008, 1.
[14] WANG Shou-jue, WANG Bai-nan(王守觉，王柏南). Acta Electronica Sinica(电子学报), 2002, 30(1): 1.
[15] YANG Guo-wei, WANG Shou-jue, LIU Yang-yang(杨国为，王守觉，刘扬阳). Acta Electronica Sinica(电子学报), 2008, 36(12): 2490.
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