Abstract
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 language | English (US) |
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Pages (from-to) | 26893-26909 |
Number of pages | 17 |
Journal | Optics Express |
Volume | 27 |
Issue number | 19 |
DOIs | |
State | Published - Sep 16 2019 |
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics