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

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 (R² = 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 (R² > 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.