Abstract：A new method of infrared spectrum analysis based on support vector machine(SVM) for mixture gas was proposed. The kernel function in SVM was used to map the seriously overlapping absorption spectrum into high-dimensional space,and after transformation,the high-dimensional data could be processed in the original space,so the regression calibration model was established,then the regression calibration model with was applied to analyze the concentration of component gas. Meanwhile it was proved that the regression calibration model with SVM also could be used for component recognition of mixture gas. The method was applied to the analysis of different data samples. Some factors such as scan interval,range of the wavelength,kernel function and penalty coefficient C that affect the model were discussed. Experimental results show that the component concentration maximal Mean AE is 0.132%,and the component recognition accuracy is higher than 94%. The problems of overlapping absorption spectrum,using the same method for qualitative and quantitative analysis,and limit number of training sample,were solved. The method could be used in other mixture gas infrared spectrum analyses,promising theoretic and application values.
白鹏1,2,谢文俊3,刘君华1 . 混合气体红外光谱支持向量机分析的新方法[J]. 光谱学与光谱分析, 2007, 27(07): 1323-1327.
BAI Peng1,2,XIE Wen-jun3,LIU Jun-hua1. New Method of Mixed Gas Infrared Spectrum Analysis Based on SVM. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27(07): 1323-1327.
[1] XU Guang-tong,YUAN Hong-fu,LU Wan-zhen(徐广通,袁洪福,陆婉珍). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2000,20(2): 134. [2] LU Wan-zhen,YUAN Hong-fu,XU Guang-tong(陆婉珍,袁洪福,徐广通). Modern Near Infrared Spectroscopic Analysis Techniques(现代近红外光谱分析技术). Beijing: China Petrochemical Press(北京: 中国石化出版社),2000. [3] CHU Xiao-li,YUAN Hong-fu,LU Wan-zhen(褚小立,袁洪福,陆婉珍). Progress in Chemistry(化学进展),2004,16(4): 528. [4] Phil Williams Kari Norris. Near-Infrared Technology in the Agricultural and Food Industries. Minnesota,USA: American Association of Cereal Chemists Inc St Paul,1987. 169. [5] Hidajat K,Chong S M. Near Infrared Spectrosc，2000,(8): 53. [6] WANG Hui,ZHAO Xiu-ge,XIAO Wen-de,et al(王 辉,赵秀阁,肖文德,等). Journal of East China University of Science and Technology(华东理工大学学报),2001,27(1): 1. [7] LI Zhi-zhang,XIANG Jian-nan,HU Fang,et al(李治章,向建南,胡 芳,等). Analytical Instrumentation(分析仪器),1995,(4): 13. [8] SUN Xiu-yun,LI Yan,WANG Jun-de(孙秀云,李 燕,王俊德). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2003,23(4): 739. [9] QU Jian-ling,WANG Lei,GAO Feng(曲建岭,王 磊,高 峰). Chinese Journal of Scientific Instrument(仪器仪表学报),2000,21(5): 471. [10] GAO Jian-bo,HU Dong-cheng(高建波,胡东成). Journal of Tsinghua University (Science and Technology)(清华大学学报·自然科学版),2001,41(3): 121. [11] DING Hui,LIU Jun-hua,SHEN Zhong-ru(丁 晖,刘君华,申忠如). Chinese Journal of Scientific Instrument(仪器仪表学报)，2002,23(6): 592. [12] Vapnik V N. The Nature of Statistical Learning. New York: Springer,1995. [13] Vapnik V N. Statistical Learning Theory. New York: John Wiley & Sons Inc,1998. [14] Vapnik V N. IEEE Trans on Neural Networks，1999,10(5): 988. [15] ZHANG Lu-da,SU Shi-guang,WANG Lai-sheng,et al(张录达,苏时光,王来生,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2005,25(1): 33. [16] MA Xiao-guang,HU Fei(马晓光,胡 非). Progress in Natural Science(自然科学进展),2004,14(3): 349. [17] CHEN Nian-yi,LU Wen-cong,YE Chen-zhou,et al(陈念贻,陆文聪,叶晨洲,等). Computers and Applied Chemistry(计算机与应用化学),2002,19(6): 691. [18] Chih Wei-hsu,Chih Jen-lin. IEEE Trans on Neural Networks，2002,13(2): 415. [19] Chih-Chung Chang,Chih-Jen Lin. Software available at http://www.csie.ntu.edu.tw /～cjlin/libsvm,2001. [20] WANG Ri-shuang(王日爽). Functional Analysis and Optimal Theories(泛函分析与最优化理论). Beijing: Beijing University of Acronautics and Astronautics Press(北京: 北京航空航天大学出版社),2003.
