Rapid and stable detection of three main mycotoxins in rice using SERS optimized AgNPs@K30 coupled multivariate calibration. (1st January 2023) - He, Peihuan; Mehedi Hassan, Md; Yang, Wenjing; Shi, Zhaoxia; Zhou, Xinyu; Xu, Yi; Ouyang, Qin; Chen, Quansheng
Graphical abstract: Highlights: Rough flower-like silver nanoparticles (K30) were comparatively and optimally used. Surface-enhanced Raman scattering and tentative assignment of mycotoxins were sought. Genetic algorithm-partial least square regression quantified mycotoxins best in rice. The limit of detection obtained was lower than the value set by European Commission. The strategy showed high stability and repeatability in detecting three mycotoxins. 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 × 10 7 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.