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.

机器学习在许多方面改变了我们的生活。在分析科学中，机器学习提供了前所未有的机会，可以从色谱，质谱，NMR和光谱等复杂或大型数据集中提取信息。在拉曼和表面增强拉曼散射（SERS）技术中尤其如此，其中复杂化学混合物的振动光谱被获取为用于分析或成像化学系统的大型数据集。经典的线性信息处理方法已不再足够，因此最近已经实现了从拉曼和SERS实验中提取化学信息的机器学习方法。在这篇评论中，我们将简要概述拉曼采用的最常见的机器学习技术，