Robust gas quantification in mid-infrared FTIR spectroscopy via a suppression-adaptation-optimization model.

Area of Science:

• Spectroscopy
• Analytical Chemistry
• Environmental Monitoring

Background:

• Mid-infrared Fourier transform infrared (FTIR) spectroscopy offers high sensitivity for multi-gas detection.
• Instrumental noise and environmental factors challenge accurate FTIR quantification.

Purpose of the Study:

• To develop a robust quantification model for FTIR spectroscopy under practical conditions.
• To mitigate spectral fluctuations and interferences affecting gas concentration retrieval.

Main Methods:

• A suppression-adaptation-optimization (SAO) model was developed, integrating noise suppression, residual adaptation, and loss function optimization.
• The model utilizes a physics-based forward model for residual correction and iterative optimization.
• A generalized loss function and the Yogi optimizer were employed for enhanced performance.

Main Results:

• The SAO model effectively mitigates the quantitative impact of spectral deviations.
• Compared to the Levenberg-Marquardt method, SAO reduced concentration standard deviation by at least 15% in simulations and up to 20% in experiments.
• The model demonstrated robustness in retrieving CO2, N2O, and CO concentrations from noisy spectra.

Conclusions:

• Integrating noise suppression and residual correction significantly enhances FTIR gas quantification robustness.
• The SAO model shows potential for reliable industrial monitoring applications requiring precise gas detection.