Abstract
Food safety is regarded as a crucial factor in both human health and economic progress. This study focuses on the fabrication of a label-free surface-enhanced Raman scattering (SERS) sensor for rapid sensing of three main mycotoxins (aflatoxin B1 (AFB1), ochratoxin A (OTA), ochratoxin B (OTB)) in rice using the optimized rough silver nanoparticles (AgNPs@K30) with enhancement factor (EF) 1.58 × 107 coupled multivariate calibration. Two variable selection chemometric calibration methods were comparatively applied. And genetic algorithm-partial least square achieved optimum correlation coefficient = 0.9797, 0.9779, and 0.9827, respectively for AFB1 ranging from 0.5 to 250 µg/Kg, for OTA and OTB 1 to 500 µg/Kg. The limit of detection (LOD) = 1.145, 1.133, and 1.180 µg/Kg, respectively, were determined according to principal component analysis-calibrated loading weight approach. And the great stability and reproducibility revealed the prepared SERS sensor has the potential to predict AFB1, OTA, and OTB in real rice samples.
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