Therefore, the accuracy of gas mixture component identification is improved by analyzing IR absorption spectroscopy. Li and others (8) proposed to use continuous wavelet transform to improve the resolution of peaks, which inputted the initial parameters into the curve fitting to get a better fitting effect. When the characteristic peaks of the components in the mixture are few and the concentration is low, or when the mixture constructed by different components has similar Raman spectral characteristics, there is a problem that the component characteristics are difficult to fully mine and the performance is reduced. Zhu and others (7) proposed a sparse non-negative least squares (SNNLS) algorithm for the qualitative analysis of mixtures in Raman spectroscopy. Wang and others (6) studied the simulation and analysis of the second harmonic signal in tunable diode laser absorption spectroscopy (TDLAS) technology to provide a reference for subsequent gas detection experiments. Many scientific research teams have done a lot of feasibility studies in the identification of spectral components and have achieved important results. This study applied the second derivative spectrum to qualitative analysis. The second derivative spectrum can remove some of the high-frequency noise and mutual interference between components (5). Therefore, it can improve the resolution of the spectrum. Derivative spectroscopy can not only distinguish aliasing peaks to a certain extent, but it can also eliminate the influence of baseline drift. The most commonly used pretreatment method in spectral quantitative analysis is derivative spectroscopy. Currently, the quantitative detection methods of IR absorption spectroscopy include techniques such as surface-enhanced IR absorption (SEIRA) spectroscopy (1,2), Fourier transform IR (FT-IR) spectroscopy (3), and cavity ring-down spectroscopy (CRDS) (4). Because of the advantages of noncontact, fast response, and high accuracy, especially in chemical composition analysis and gas mixture identification, it is widely used. Infrared (IR) absorption spectroscopy technology is an analysis method that has developed rapidly in recent years. The research in this article provides new ideas for the quantitative detection of gases. The frequency correlation analysis at the characteristic absorption position can improve the recognition accuracy compared with the frequency correlation analysis in the entire spectral interval. The experimental results showed that the correlation analysis of the time dimension can extract the characteristic absorption position of the gas to be measured in the gas mixture. The correlation analysis of time and frequency on the time-frequency characteristic matrix was used for component identification. The appropriate scale range was selected through the variance of wavelet coefficients. The second derivative spectrum of the IR absorption spectroscopy was processed by continuous wavelet transform to obtain the time-frequency characteristic matrix. To solve the problem of spectrum line aliasing in gas detection, this study examined the application of IR absorption spectroscopy technology based on time-frequency analysis in component identification. Because infrared (IR) absorption spectroscopy technology can offer high sensitivity and strong anti-interference capabilities, it is widely used in gas detection.
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