Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review

Surface-enhanced Raman spectroscopy (SERS) has gained significant attention for its ability to detect environmental contaminants with high sensitivity and specificity. The cost-effectiveness and potential portability of the technique further enhance its appeal for widespread application. However, challenges such as managing high-dimensional data, detecting low-concentration targets amid environmental interferents, and navigating overlapping spectral peaks remain. In response, there is a growing trend toward using machine learning (ML) approaches that encompass multivariate tools for effective SERS data analysis. This comprehensive review details key steps for applying ML techniques to SERS, surveys environmental applications where ML tools are integrated for detecting pathogens and (in)organic pollutants, and discusses the considerations and benefits of ML in these contexts. The review also outlines opportunities for synergizing SERS with ML for real-world deployments.