SERS-Based Detection of Pesticide Residues in Food: Substrate Fabrication, Optimization, and Applications

Abstract

Surface-enhanced Raman scattering (SERS) technology, with its molecular fingerprint recognition, high sensitivity, rapid detection, and non-destructive analysis, has become a key research direction in the detection of pesticide residues in food. However, critical challenges remain, including poor uniformity, insufficient stability, and difficulty in large-scale fabrication of SERS substrates, as well as interference from complex food matrices that affects detection accuracy. This review systematically summarizes the most commonly used preparation and optimization strategies for the SERS substrates, which provides theoretical support and practical references for the rational design of SERS substrates, the improvement of fabrication processes, and the development of anti-interference algorithms. Furthermore, focusing on the detection advances (2020–present) of four typical classes of pesticide residues (organophosphorus, benzimidazole, neonicotinoid, and carbamate) in food, this review elaborates on the core SERS detection principles, technical advantages, practical limitations, and corresponding solution strategies. This work clarifies the research advances and technical challenges of SERS in the detection of pesticide residue in food, offering clear guidance and references for improving the technical framework and promoting future research in this field.