New method of mixed gas infrared spectrum analysis based on SVM

作者：Bai Peng^*; Xie Wen jun; Liu Jun Hua

来源：Spectroscopy and Spectral Analysis, 2007, 27(7): 1323-1327.

• support vector machine
• regression
• calibration model
• infrared spectrum
• quantitative analysis
• qualitative analysis

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.

• 出版日期2007-7
• 单位中国人民解放军空军工程大学; 西安交通大学