Physics-informed neural networks for etaloning correction in Raman spectra using inverse modeling

Raman spectroscopy, valued for its ability to provide detailed molecular fingerprints, plays a crucial role in industrial and academic research. However, optical artifacts, such as etaloning, often compromise the spectral quality, introducing unwanted intensity variations. Our research focuses on correcting these inaccuracies through inverse modeling, utilizing deep learning (DL). We used a traditional data-driven DL model to establish a performance baseline for etaloning correction. We then introduced a novel approach by integrating Physics-informed neural networks (PINNs), which incorporate underlying physical laws, to improve the accuracy of etaloning correction. Both methods were compared using design-out cross-validation to assess their generalization. We trained the models on synthetic data generated via forward modeling and fine-tuned them using real spectra. Our results demonstrate that PINNs outperform traditional DL methods, offering a more robust solution for etaloning correction in Raman spectra.