Advances in deep learning-based applications for Raman spectroscopy analysis: A mini-review of the progress and challenges

Raman spectroscopy is a non-invasive, label-free characterization technique that provides detailed chemical information about samples, particularly of complex chemical mixtures such as liposomes, lipid droplets, and whole cells. However, extracting precise chemical information, such as component concentrations, from these multicomponent mixtures remains a persistent challenge in Raman spectral analysis. This review provides a concise overview of the technical and theoretical foundations of Raman spectroscopy, its working principles, and the current challenges associated with its application. It also highlights up-to-date and emerging uses of deep learning methods in Raman spectroscopy, including approaches for spectral preprocessing, classification, and regression. Finally, the review discusses the obstacles in developing deep learning models for Raman spectroscopy and provides insights to propel future research in this field.