Automated Background Noise Removal from Substrate Materials in Raman Spectra Using Dynamic Time Warping

Raman spectroscopy is a recognized powerful analytical tool in various fields such as point-of-care testing and environmental monitoring applications. However, the presence of background signals, from matrix interference or/and substrate materials (e.g., silicon or polydimethylsiloxane) support use, often poses a significant impediment to the accurate interpretation of spectral data. Recognizing the importance of background subtraction in the analytical process, this article introduces an innovative automated methodology designed to refine the clarity of Raman spectra. Our approach systematically subtracts background information, facilitating subsequent data analysis tasks within the spectra. The cornerstone of this method lies in the meticulous extraction of salient features from the background spectrum, followed by the precise removal of these features from the measured spectrum. We have rigorously tested our proposed method's efficacy via simulated and experimental data sets, demonstrating its robustness and reliability. The results underscore the potential of our automated technique to serve as a transformative tool in enhancing Raman spectroscopic analysis.