Applications of artificial intelligence and machine learning in combination with surface-enhanced Raman spectroscopy (SERS)

Surface-enhanced Raman spectroscopy (SERS) offers exceptional sensitivity for identifying and detecting a wide range of compounds by greatly enhancing Raman signals from molecules on metal surfaces. SERS application has been further transformed by the integration of artificial intelligence (AI) and machine learning (ML), which automates spectrum interpretation, enhances identification accuracy, and optimizes experimental settings. This paper examines current developments in the synergistic use of AI and ML with SERS in a variety of disciplines, including environmental monitoring, food safety, pathogen detection, and disease diagnosis. Studies that have been published show that these models can distinguish between analytes such as bacteria, viruses, cancer cells, and chemical substances with above 95 % accuracy. The promise of these methods is shown by the fact that some research even showed 100 % accuracy in sample identification. Food safety, environmental monitoring, and clinical diagnostics might all be revolutionized by SERS-ML techniques because of their great sensitivity, specificity, and reliability. Future research should focus on extending clinical applications, enhancing substrate capabilities and detection limitations, incorporating sophisticated machine learning techniques, and increasing the application broadness. In order to improve the robustness and practicality of these methodologies, further validation in larger cohorts and real-world contexts is also emphasized. The study demonstrates how combining AI/ML with SERS offers the potential to fundamentally change the fields of materials research, environmental monitoring, diagnostics, and other related fields.