We report the studies on the automatic extraction of the Raman signal from coherent anti-Stokes Raman scattering (CARS) spectra by using a convolutional neural network (CNN) model. The model architecture is adapted from literature and retrained with synthetic and semi-synthetic data. The synthesized CARS spectra better approximate the experimental CARS spectra. The retrained model accurately predicts spectral lines throughout the spectral range, even with minute intensities, which demonstrates the potential of the model. Further, the extracted Raman line-shapes are in good agreement with the original ones, with an RMS error of less than 7% on average and have shown correlation coefficients of more than 0.9. Finally, this approach has a strong potential in accurately estimating Raman signals from complex CARS data for various applications.

Typical schematic of the CNN model architecture trained with CARS data generated from different non resonant backgrounds. The input is a CARS spectrum and the output is a Raman spectrum.

Coherent anti-Stokes Raman scattering (CARS) is a well-known third-order non-linear spectroscopic technique utilized to analyze the molecule structures. However, it has a coherent non-resonant background (NRB) alongside with Raman resonant, thus, it is difficult to extract the Raman part. In this work, the Raman signal is extracted from the CARS spectrum by using a convolutional neural network. The model architecture is adapted from the original SpecNet model. The model is pre-trained with synthetic data and fine-tuned with semi-synthetic data based on two sets of semi-synthetic spectra. The experimental results show that the model achieves 86% accuracy in predicting the Raman signal of semi-synthetic data. In addition, the sensitivity of the model performance to varying levels of noise is analysed. Based on standard metrics, the model performance decreases with the increasing level of noise in a non-linear manner. Finally, the prediction capability of the fine-tuned SpecNet model was evaluated on the four experimental CARS spectra and the results were found to be better compared to SpecNet.

The non-resonant background (NRB) contribution to the Coherent anti-Stokes Raman scattering (CARS) signal distorts the spectral line shapes and thus degrades the chemical information. Hence, finding an effective approach for...