Optimization of a surface-enhanced Raman spectroscopy substrate for detection of pesticide residues on food peels

Increasing concerns regarding food safety and environmental contamination have driven the development of analytical methods capable of detecting pesticide residues at trace levels. Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful technique due to its high sensitivity, minimal sample preparation, and rapid data acquisition. However, achieving high reproducibility and low detection limits with SERS substrates remains a significant challenge. This study aims to optimize a SERS substrate composed of reduced graphene oxide/silver nanoparticles (rGO/AgNPs) thin films, synthesized via a liquid-liquid interfacial route, for the direct detection of pesticide residues on food peels. A systematic multivariate optimization strategy, incorporating factorial and Box-Behnken experimental designs, was employed to enhance the SERS performance of the substrate. The optimized substrate exhibited a ∼ 21,500-fold enhancement in SERS signal intensity compared to conventional Raman spectroscopy, and an 8-fold increase relative to the non-optimized synthesis. The optimized substrate enabled the detection of the herbicide Ametryn on apple and potato peels at concentrations as low as 1.0 × 10^-7 mol L^-1. Hyperspectral imaging of wide sample areas minimized the inherent variability of SERS, improving detection limits and allowing reliable identification of Ametryn residues with minimal sample preparation. This work demonstrates the potential of SERS as a rapid, cost-effective, and environmentally friendly alternative to conventional pesticide detection methods. Furthermore, it underscores the value of multivariate optimization in fine-tuning substrate synthesis for enhanced analytical performance.