Objective To enhance the safety and efficiency of hazardous gas detection while minimizing operational risks, this study investigates a quantitative analysis method for high hydrogen sulfide(H₂S)-containing gas mixtures based on the high-resolution transmission molecular absorption database (HITRAN database), and further validates the feasibility of its application in the fields of industrial, environmental monitoring, and public safety.

Method Fourier transform infrared (FTIR) spectroscopy was employed in conjunction with support vector regression (SVR) and radial basis function (RBF) neural network models to perform quantitative analysis on gas mixtures containing H₂S, CO₂, and CH₄. High-precision theoretical spectra data were generated using the HITRAN database, and a spectral superposition method was applied to simulate the infrared spectra of gas mixtures. The noise was added to simulate the response characteristics of FTIR instruments, making the simulated spectra closer to real detection scenarios.

Result The proposed method demonstrated high efficiency and precision in the quantitative analysis of multi-component gas mixtures. The radial basis function kernel-based SVR (R-SVR) model outperformed the RBF neural network model, achieving higher detection precision.

Conclusion This study provides a low-cost, efficient, and safe simulation-based validation method for detecting high H₂S-containing gas mixtures. It offers reliable technical support for multi-component gas mixtures detection in practical applications and holds significant value for engineering practices.