Information density-based adaptive band selection for wide dynamic range gas quantification in infrared spectroscopy

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Environmental Science

Background:

• Accurate gas quantification is crucial for environmental monitoring and industrial safety.
• Fourier-transform infrared (FTIR) spectroscopy offers broad detection but struggles with nonlinear responses at high concentrations due to saturation, resolution limits, and baseline errors.

Purpose of the Study:

• To develop a novel method for accurate gas quantification using FTIR spectroscopy, overcoming limitations at elevated concentrations.
• To enhance the dynamic range and reliability of FTIR-based gas analysis for complex mixtures.

Main Methods:

• Proposed the information density-based adaptive band selection (ID-ABS) method.
• Integrated spectral line intensity, absorption saturation, instrument line shape, and baseline features.
• Employed nonlinear multivariate regression with iterative parameter updates for optimal band selection.

Main Results:

• Achieved highly accurate methane quantification with a linear dynamic range of 3 × 10⁷ (R² = 0.9998).
• Demonstrated the model's ability to dynamically select optimal inversion parameters for each component based on information density.

Conclusions:

• The ID-ABS method significantly enhances FTIR quantitative analysis capabilities, especially for complex mixtures.
• The model's applicability extends to various gases with infrared absorption features, improving environmental and industrial monitoring.