Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering

Abstract Machine learning is shaping up our lives in many ways. In analytical sciences, machine learning provides an unprecedented opportunity to extract information from complex or big datasets in chromatography, mass spectrometry, NMR, and spectroscopy, among others. This is especially the case in Raman and surface-enhanced Raman scattering (SERS) techniques where vibrational spectra of complex chemical mixtures are acquired as large datasets for the analysis or imaging of chemical systems. The classical linear methods of processing the information no longer suffice and thus machine learning methods for extracting the chemical information from Raman and SERS experiments have been implemented recently. In this review, we will provide a brief overview of the most common machine learning techniques employed in Raman, a guideline for new users to implement machine learning in their data analysis process, and an overview of modern applications of machine learning in Raman and SERS.