  : 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.
