Investigating the effect of non-resonant background variation on the CARS data analysis and classification

TL;DR

This study systematically examines how variations in non-resonant background affect Raman signal retrieval in CARS spectroscopy, comparing multiple methods and highlighting the importance of normalization and background removal.

Contribution

It is the first comprehensive investigation into the impact of NRB variation on CARS data analysis and classification, comparing traditional and deep learning methods.

Findings

MEM and KK outperform LSTM and CNN at high NRB levels

Normalizing input data improves deep learning model predictions

Background removal influences correlation but not classification accuracy

Abstract

: Non-resonant background (NRB) plays a significant role in coherent anti-Stokes Raman scattering (CARS) spectroscopic applications. All the recent works primarily focused on removing the NRB using different deep learning methods, and only one study explored the effect of NRB. Hence, in this work, we systematically investigated the impact of NRB variation on Raman signal retrieval. The NRB is simulated as a linear function with different strengths relative to the resonant Raman signal, and the variance also changed for each NRB strength. The resonant part of nonlinear susceptibility is extracted from real experimental Raman data; hence, the simulated CARS data better approximate the experimental CARS spectra. Then, the corresponding Raman signal is retrieved by four different methods: maximum entropy method (MEM), Kramers-Kronig (KK), convolutional neural network (CNN), and long short-term memory (LSTM) network. Pearson correlation measurements and principal component analysis combined with linear discriminant analysis (PCA-LDA) modelling revealed that MEM and KK methods have an edge over LSTM and CNN for higher NRB strengths. It is also demonstrated that normalizing the input data favors LSTM and CNN predictions. In contrast, background removal from the predictions significantly influenced Pearson correlation but not the classification accuracies for MEM and KK. This comprehensive study is done for the first time to the best of our knowledge and has the potential to impact the CARS spectroscopy and microscopy applications in different areas.