Recent advancements in SERS-based detection of micro- and nanoplastics in food and beverages: Techniques, instruments, and machine learning integration

Background: The presence of micro- and nanoplastics (MNPs) in food and beverages has raised significant concerns due to their potential health risks and environmental impacts. Accurate detection of MNPs in complex matrices like food and beverages is vital for protecting public health. Surface-enhanced Raman spectroscopy (SERS) enables sensitive, rapid, and non-destructive MNP detection by amplifying Raman signals with metallic nanostructures, allowing precise identification and characterization, making it a valuable tool for food safety monitoring. Scope and approach: This review examines various challenges associated with detecting MNPs using SERS. It delves into critical aspects of SERS, such as instrument calibration, substrate design, and advanced device configurations to improve detection sensitivity and reliability. Furthermore, the review examines existing research across various food and beverage categories to identify research gaps and areas that require further investigation. Integrating machine learning (ML) enhances detection accuracy, streamlines data analysis, and provides actionable insights, helping researchers optimize workflows and expand SERS applications in food safety. Key findings and conclusions: SERS has proven to be a highly effective technique for detecting MNPs in food and beverages, offering unmatched sensitivity and the ability to characterize plastic particles at trace levels in complex matrices. Innovations in substrate design and instrument configurations have significantly improved its practicality, while portable SERS devices enable real-time, on-site detection. Integrating ML with SERS enhances data interpretation, detection accuracy, and automation. This synergy strengthens SERS as a crucial tool for food safety monitoring and public health, addressing critical concerns with greater efficiency and reliability.