Quantitative analysis of multi-component gas mixture based on KPCA and SVR

• Published in: Guang pu xue yu guang pu fen xi Vol. 28; no. 6; p. 1286
• Main Authors: Hao, Hui-min, Tang, Xiao-jun, Bai, Peng, Liu, Jun-hua, Zhu, Chang-chun
• Format: Journal Article
• Language: Chinese
• Published: China 01.06.2008
• ISSN: 1000-0593

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 (phi x 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.