A1 Journal article (refereed), original research

Bayesian Quantification for Coherent Anti-Stokes Raman Scattering Spectroscopy


Open Access hybrid publication


Publication Details

Authors: Härkönen Teemu, Roininen Lassi, Moores Matthew T., Vartiainen Erik

Publisher: American Chemical Society

Publication year: 2020

Language: English

Related journal or series: Journal of Physical Chemistry B

Volume number: 124

Issue number: 32

Start page: 7005

End page: 7012

Number of pages: 8

ISSN: 1520-6106

eISSN: 1520-5207

JUFO level of this publication: 1

Digital Object Identifier (DOI): http://dx.doi.org/10.1021/acs.jpcb.0c04378

Open Access: Open Access hybrid publication


Abstract

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 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.


Last updated on 2021-16-03 at 12:47