Efficient Bayesian inference of absorbance spectra from transmitted intensity spectra

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Computational Science

Background:

• High-resolution absorption spectroscopy offers non-invasive process monitoring.
• Computational demands hinder real-time applications of absorption spectroscopy.

Purpose of the Study:

• To develop a computationally efficient method for estimating absorbance spectra.
• To enable high-speed, low-latency process monitoring using absorption spectroscopy.

Main Methods:

• Employed Bayesian statistics to combine a measurement model with prior spectral information.
• Formulated a linear least-squares problem, shifting computational load to a preparation step.
• Validated the method on simulated tunable diode laser absorption spectroscopy data with noise and fringes.

Main Results:

• Achieved highly accurate absorbance spectra estimation.
• Demonstrated significant computational efficiency with a processing time of 2 ms per spectrum.
• Successfully handled simulated data with additive noise and fluctuating fringes.

Conclusions:

• The developed Bayesian method is computationally efficient and accurate for real-time spectral analysis.
• This approach overcomes computational limitations for high-speed absorption spectroscopy applications.
• Facilitates rapid, non-invasive process monitoring in demanding industrial environments.