Infrared spectra analysis method of multi-component mixed gas concentration based on support vector machine

Wenjun Xie; Peng Bai; Lei Xiao

doi:10.1117/12.783632

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27 November 2007 Infrared spectra analysis method of multi-component mixed gas concentration based on support vector machine

Wenjun Xie, Peng Bai, Lei Xiao

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Proceedings Volume 6723, 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment; 67234D (2007) https://doi.org/10.1117/12.783632
Event: 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Large Mirrors and Telescopes, 2007, Chengdu, China

Abstract

Component concentration analysis of mixed gas is an important part for measurement. In this study a regression model with support vector machine using a data set with 5000 samples is developed and applied to predicting unknown component concentration of mixed gas. Through transformation of kernel function, multidimensional and overlapped spectrum data is mapped into high dimension space, so that regression model of mixed gas is carried out in high dimension space of support vector. Some factors such as unitary process, scan interval, range of wavelength, kernel functions and penalty coefficient C that affect model are discussed. Experimental results show that component concentration maximal error is 1.45%. The difficulties of overlapping feature spectrum, identical method of mixed gas analysis, limit number of training sample and dimension of input spectrum are solved and the model brings important theoretical and applied value.

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Wenjun Xie, Peng Bai, and Lei Xiao "Infrared spectra analysis method of multi-component mixed gas concentration based on support vector machine", Proc. SPIE 6723, 3rd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 67234D (27 November 2007); https://doi.org/10.1117/12.783632

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