: 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.

翻译：非共振背景在相干反斯托克斯拉曼散射光谱应用中具有重要作用。近期研究主要集中于利用不同深度学习方法消除非共振背景，仅有一项工作探讨了非共振背景的影响。因此，本研究系统性地考察了非共振背景变化对拉曼信号提取的影响。非共振背景被模拟为相对于共振拉曼信号具有不同强度的线性函数，且每个非共振背景强度对应的方差也各不相同。非线性敏感度的共振部分提取自真实实验拉曼数据，从而使模拟的CARS数据能更好地逼近实验CARS光谱。随后通过四种不同方法提取相应拉曼信号：最大熵方法、Kramers-Kronig变换、卷积神经网络以及长短期记忆网络。皮尔逊相关性测量与主成分分析结合线性判别分析建模表明，在较高非共振背景强度下，最大熵方法和Kramers-Kronig变换较LSTM和CNN具有优势。研究还证明输入数据归一化有利于LSTM和CNN的预测。相比之下，预测结果的背景消除对皮尔逊相关性影响显著，但对最大熵方法和Kramers-Kronig变换的分类准确率影响甚微。据我们所知，此项综合性研究尚属首次，有望对CARS光谱学及显微技术在多个领域的应用产生重要影响。