| 光谱学与光谱分析 |
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| Near Infrared Spectroscopy Synergy Interval Wavelength Selection Method Using the LSSVM Model |
| PENG Xiu-hui, HUANG Chang-yi, LIU Fei*, LIU Yan |
| Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Institute of Automation, Jiangnan University, Wuxi 214122, China |
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Abstract The present paper proposes a wavelength selection algorithm based on nonlinear factors named Synergy interval least squares support vector machines (siLSSVM). siLSSVM combines the interval strategy of wavelength selection method with the idea of synergy interval and overcomes the disadvantages of the traditional wavelength selection methods, i.e. ignoring the nonlinear factors. Taking the near infrared spectrum data of apple sugar as performance verification object of this new algorithm, comparing new algorithm with siPLS, the model performance has been greatly improved. The root-mean-square error (RMSEP) in new algorithm has increased respectively by 37.43% and 47.88% under the model of PLS and LSSVM, with increases of 6.04% and 7.31% in the correlative coefficient (RP). The examples illustrate that siLSSVM can efficiently select the optimum wavelength interval for spectrum data with strong nonlinear factors. This algorithm greatly improves the prediction accuracy and robustness of the model, which provides a new prospect for near infrared spectral with nonlinear factors to select wavelength.
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Received: 2013-05-22
Accepted: 2013-08-19
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Corresponding Authors:
LIU Fei
E-mail: fliu@jiangnan.edu.cn
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[1] Mario H M K, Jarbas J R R, Celio P. Fuel, 2011，90(11): 3268.
[2] LU Wan-zhen(陆婉珍). Modern Analysis Technique of Near Infrared Spectroscopy(现代近红外光谱分析技术). Beijing: China Petrochemical Press(北京: 中国石化出版社), 2007. 35.
[3] Ye Shengfeng, Wang Dong, Min Shungeng. Chemometrics and Intelligent Laboratory Systems, 2008，91(2): 194.
[4] Zou Xiaobo, Zhao Jiewen, Malcolm J W P, et al. Analytica Chimica Acta, 2010，667(1): 14.
[5] Norgaard L, Saudland A, Wagner J, et al. Applied Spectroscopy, 2000，54(3): 413.
[6] SUN Bai-ling, LIU Jun-liang, CAI Yu-bo, et al(孙柏玲，刘君良，蔡宇博，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2011, 31(2): 366.
[7] LI Jun-liang, WANG Cong-qing(李军良，王从庆). Journal of Zhejiang University·Agric.& Life Sci.(浙江大学学报·农业与生命科学版)，2011, 37(4): 453.
[8] Vapnik V N. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.
[9] Zou Hongyan, Wu Hailong, Fu Haiyan. Talanta, 2010，80(5): 1698.
[10] Li Hongdong, Liang Yizeng, Xu Qingsong. Chemometrics and Intelligent Laboratory Systems, 2009，95(2): 188.
[11] GU Yan-ping, ZHAO Wen-jie, WU Zhan-song(顾燕萍，赵文杰，吴占松). Journal of Tsinghua University·Science and Technology(清华大学学报·自然科学版), 2010, 50(7)：1068.
[12] Devos O, Ruckebusch C, Durand A. Chemometrics and Intelligent Laboratory Systems, 2009，96(1): 27.
[13] Peng Jiangtao, Jiang An, Peng Silong. Analytica Chimica Acta, 2010，667(1): 14. |
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