Efficient Bayesian inference of absorbance spectra from transmitted intensity spectra

Johannes Emmert, Samuel J. Grauer, Steven Wagner, Kyle J. Daun

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


High-resolution absorption spectroscopy is a promising method for non-invasive process monitoring, but the computational effort required to evaluate the data can be prohibitive in high-speed, real-time applications. This study presents a fast method to estimate absorbance spectra from transmitted intensity signals. We employ Bayesian statistics to combine a measurement model with prior information about the shape of the baseline intensity and absorbance spectrum. The resulting linear least-squares problem shifts most of the computational effort to a preparation step, thereby facilitating quick processing and low latency for any number of measurements. The method is demonstrated on simulated tunable diode laser absorption spectroscopy data with additive noise and a fluctuating fringe. Results were highly accurate and the method was computationally efficient, having a processing time of only 2 ms per spectrum.

Original languageEnglish (US)
Pages (from-to)26893-26909
Number of pages17
JournalOptics Express
Issue number19
StatePublished - Sep 16 2019

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics


Dive into the research topics of 'Efficient Bayesian inference of absorbance spectra from transmitted intensity spectra'. Together they form a unique fingerprint.

Cite this