Bayesian Quantification for Coherent Anti-Stokes Raman Scattering Spectroscopy
| Autor: | Lassi Roininen, Teemu Härkönen, Erik M. Vartiainen, Matthew T. Moores |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Materials science Context (language use) Spectrum Analysis Raman 010402 general chemistry Statistics - Applications 01 natural sciences Article symbols.namesake Wavelet 0103 physical sciences Materials Chemistry Applications (stat.AP) Physical and Theoretical Chemistry Spectral resolution Uncertainty quantification Spectroscopy 010304 chemical physics Water Bayes Theorem 0104 chemical sciences Surfaces Coatings and Films symbols Artifacts Biological system Raman spectroscopy Raman scattering Interpolation |
| Zdroj: | The Journal of Physical Chemistry. B |
| ISSN: | 1520-5207 1520-6106 |
| DOI: | 10.1021/acs.jpcb.0c04378 |
| Popis: | We propose a Bayesian statistical model for analyzing coherent anti-Stokes Raman scattering (CARS) spectra. Our quantitative analysis includes statistical estimation of constituent line-shape parameters, underlying Raman signal, error-corrected CARS spectrum, and the measured CARS spectrum. As such, this work enables extensive uncertainty quantification in the context of CARS spectroscopy. Furthermore, we present an unsupervised method for improving spectral resolution of Raman-like spectra requiring little to no \textit{a priori} information. Finally, the recently-proposed wavelet prism method for correcting the experimental artefacts in CARS is enhanced by using interpolation techniques for wavelets. The method is validated using CARS spectra of adenosine mono-, di-, and triphosphate in water, as well as, equimolar aqueous solutions of D-fructose, D-glucose, and their disaccharide combination sucrose. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|