1. School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China 2. Taiyuan Iron and Steel Co., Automatic Company, Taiyuan 030003, China 3. Engineering Institute, Air Force Engineering University, Xi’an 710038, China
Abstract:In the present paper, the authors present a new quantitative analysis method of mid-infrared spectrum. The method combines the kernel principal component analysis (KPCA) technique with support vector regress machine (SVR) to createa quantitative analysis model of multi-component gas mixtures. Firstly,the spectra of multi-component gas mixtures samples were mapped nonlinearly into a high-dimensional feature space through the use of Gaussian kernels. And then, PCA technique was employed to compute efficiently the principal components in the high-dimensional feature spaces. After determining the optimal numbers of principal components, the extracted features (principal components) were used as the inputs of SVR to create the quantitative analysis model of seven-component gas mixtures. The prediction RMSE (φ×10-6) of seven-component gases of prediction set samples by use of KPCA-SVR model were respectively 124.37, 72.44, 136.51, 87.29, 153.01, 57.12, and 81.72, ten times less than that by use of SVR model. The elapsed time of modeling and prediction by using KPCA-SVR were respectively 46.59 (s) and 4.94 (s), which was consumedly less than 752.52 (s) and 26.21 (s) by using only SVR. These results show that KPCA has an excellent ability of nonlinear feature extraction. It can make the most of the information of entire spectra range and effectively reduce noise and the dimension of the spectra. The KPCA combined with SVR can improve the model’s analysis precision and cut the elapsed time of modeling and analysis. From our research and experiments, we conclude that KPCA-SVR is an effective new method for infrared spectroscopic quantitative analysis.
郝惠敏1,2,汤晓君1,白鹏1,3,刘君华1,朱长纯1. 基于核主成分分析和支持向量回归机的红外光谱多组分混合气体定量分析[J]. 光谱学与光谱分析, 2008, 28(06): 1286-1289.
HAO Hui-min1,2,TANG Xiao-jun1,BAI Peng1,3,LIU Jun-hua1,ZHU Chang-chun1. Quantitative Analysis of Multi-Component Gas Mixture Based on KPCA and SVR. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2008, 28(06): 1286-1289.
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