Raman signal extraction from BCARS intensity measurements using deep learning with a prior excitation profile

¹ Department of Electronic Engineering, Maynooth University, Co. Kildare, Ireland
² Department of Computer Science, Maynooth University, Maynooth, Co. Kildare, Ireland
³ Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, USA

^* e-mail: ryan.muddiman.2021@mumail.ie
^** e-mail: bryan.hennelly@mu.ie

Published online: 18 October 2023

Abstract

Broadband Coherent anti-Stokes Raman Scattering (BCARS) microscopy is a useful technique for chemical analysis and allows the full vibrational fingerprint spectrum of a specimen to be obtained in millisec-onds. A major drawback to this technique is the presence of the non-resonant background response producing interference which prevents classical spectral analysis of the sample. Using a convolutional autoencoder and measurements of the laser characteristics, we have shown that it is possible to remove this background with-out requiring supervision, as is typically required for conventional removal methods. This approach therefore simplifies the analysis of hyperspectral images obtained with BCARS.