Ultra-sensitive SERS-ICA for carbofuran detection in complex food matrices: a smart signal averaging strategy overcoming matrix interference

The accurate detection of pesticide residues in complex food matrices is significantly hindered by interference from sample components. To address this challenge, we developed a novel surface-enhanced Raman spectroscopy-based immunochromatographic assay (SERS-ICA) for the ultrasensitive and on-site detection of carbofuran. The method employs antibody-conjugated Au@Ag core-shell nanoparticles (Au@Ag NPs) as SERS probes, introducing the Raman reporter molecule 4-mercaptobenzoic acid (4MBA) with stable and easily identifiable characteristic peaks. A competitive immunoassay format was established, leveraging antigen-antibody specificity for selective carbofuran recognition. To maximize detection reliability, we systematically evaluated signal-averaging strategies for SERS-ICA by comparing Raman signals collected at 1, 2, 4, 8, and 16 test-line positions. We identified an eight-point averaging scheme that balances precision (R 2  = 0.9934) and practicality (average recovery = 99.39%). The assay demonstrates remarkable sensitivity with a Raman detection limit of 1 pg/kg (10 −6  mg/kg) and a visual cutoff of 10 μg/kg (10 −5  mg/kg). Validation with three agriculturally relevant vegetables (celery, cowpea, and cucumber) showed excellent performance, with linear correlations (R 2  > 0.99) and near-quantitative recoveries (99.09–99.39%). Blind spike-recovery tests (95.47–101.65%) further confirmed the robustness of the method for real-world applications. This work establishes SERS-ICA as a next-generation platform for field-deployable, matrix-resistant pesticide monitoring with unmatched sensitivity.