A new method for FTIR spectral quantitative analysis was presented. The new method couples kernel partial least squares(KPLS) feature extraction with support vector regression machine(SVR) to improve the quantitative analysis accuracy and speed of seven-component alkane gas mixtures composed of methane, ethane, propane, iso-butane, n-butane, isopentane, and n-pentane, whose feature absorption spectra are cross each other and overlapped seriously. Firstly, the KPLS was employed to extract feature components from the FTIR spectra of above-mentioned seven-component gas mixtures. And then, the extracted feature components were fed into SVR to create the quantitative analysis model of seven component gases. The quantitative analysis results of calibration gas mixtures show that the prediction accuracy by KPLS-SVR model is higher than that by SVR model without feature extraction processing. Meanwhile, the predicting time by KPLS-SVR model is only half of that by SVR model. The study indicates that KPLS approach can effectively extract the latent nonlinear features implied in the spectra and component concentration, eliminate the noise of FTIR spectral data, and reduce the dimension of the spectral data. Coupling with SVR, KPLS feature extraction can improve the accuracy of FTIR spectral analysis, shorten the predicting time. KPLS-SVR is a very effective method for infrared spectral quantitative analysis.