Advancing SERS-based detection of micro and nanoplastics in Agroecosystems: Current progress, challenges, and future directions

Micro- and nanoplastics (MNPs) in agro-ecosystems pose serious risks to health and the environment. Their detection is crucial for understanding their occurrence, transport, and toxicity effects, especially in soil and plant systems. However, conventional detection tools often face challenges such as poor reproducibility, high costs, and limited on-site practical application. Point-of-care (POC) tools have emerged as critical solutions, with cutting-edge immunoassays, such as surface-enhanced Raman spectroscopy (SERS), gaining prominence due to their ultra-high sensitivity, specificity, and selective vibrational profiling capabilities. These attributes enable the precise identification of target molecules at low concentrations with minimal false negatives, making SERS a transformative approach in MNPs detection. This review highlights the presence and impact of MNPs in agroecosystems, focusing on their entry, movement, accumulation, and effect on plant and soil microbial systems. It highlights the potential of SERS as an effective detection method, explaining its principles, recent advancements, and applications to real agricultural samples. The discussion also includes the design of efficient SERS substrates essential for accurate MNPs detection and explores pre-treatment strategies that make SERS more applicable in field conditions. Key findings indicate that SERS provides unmatched sensitivity for detecting MNPs in complex agricultural matrices. Innovations in substrate engineering and portable device development have improved real-time, on-site monitoring capabilities. Integrating machine learning (ML) with SERS further enhances detection accuracy and automation. Together, these advances position SERS as a powerful tool for food safety and environmental monitoring, offering reliable solutions to a growing global concern. • MNPs' origins and pathways for understanding their distribution were reviewed. • SERS was proposed as the most efficient and appropriate analytical method for analyzing MNPs. • This review outlined the challenges in MNPs detection by SERS. • Integrating machine learning SERS for future directions and possible solutions was proposed.