Removing non-resonant background from broadband CARS using a physics-informed neural network-Reference-Cited by-同舟云学术

Removing non-resonant background from broadband CARS using a physics-informed neural network

Published:2023 Issue:32 Volume:15 Page:4032-4043
ISSN:1759-9660
Container-title:Analytical Methods
language:en
Short-container-title:Anal. Methods

Author:

Muddiman Ryan¹^ORCID,O' Dwyer Kevin¹,Camp Charles. H.²,Hennelly Bryan¹³^ORCID

Affiliation:

1. Department of Electronic Engineering, Maynooth University, Co. Kildare, Ireland

2. Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, USA

3. Department of Computer Science, Maynooth University, Co. Kildare, Ireland

Abstract

Broadband coherent anti-Stokes Raman scattering (BCARS) is capable of producing high-quality Raman spectra spanning broad bandwidths, 400–4000 cm−1, with millisecond acquisition times. We present an approach to removing the non-resonant background using a physics-informed autoencoder.

Funder

Science Foundation Ireland

Publisher

Royal Society of Chemistry (RSC)

Subject

General Engineering,General Chemical Engineering,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/AY/D3AY01131C

Reference27 articles.

1. Simple approach to one-laser, broadband coherent anti-Stokes Raman scattering microscopy

2. High-speed coherent Raman fingerprint imaging of biological tissues

3. Polarization coherent anti-Stokes Raman scattering microscopy