Data Preprocessing Method for Infrared Spectra Analysis of Natural Gas Components

When using infrared spectroscopy to analyze the components of natural gas, the obtained spectral signals often contain interference from stray light, noise, baseline drift, and other factors, which affects the resulting quantitative analysis. Therefore, it is necessary to preprocess the original spectrum before modeling. As a potential solution, an SG smoothing method combined with the soft threshold denoising method of the sym6 wavelet function was proposed to preprocess the spectrogram. The traditional preprocessing method and the proposed method are compared and analyzed. The results show that when the proposed method is used to preprocess the spectrogram, the highest goodness of fit value is 0.98652, and the lowest residual sum of squares value is 5.50694, which proves that the function peak fitting effect is the best after using this method, and the processing effect is better than that of the traditional method.