Support vector regression-based study of interference in absorption spectral lines of mixed gases

Abstract:

When measuring the concentration of multi-component gas mixtures based on supercontinuum laser absorption spectroscopy (SCLAS), there are interferences between the absorption spectral lines. For the spectral interference problem of CO2 and CH4 at 1 432 nm, a method based on support vector regression (SVR) is proposed in this paper. The SVR model, the k-nearest neighbor (KNN) model and the least squares (LS) model are used to analyze and predict the absorption spectral data, and the prediction accuracies were 96.29%, 88.89% and 85.19%, respectively, with the highest prediction accuracy of the SVR model. The results show that the method can accurately measure the concentration of gas mixtures, realize the detection of mixed gases using a single waveband, and provide a solution to the overlapping spectral line interference of multi-component gas mixtures.