Rapid detection and prediction of chloramphenicol in food employing label-free HAu/Ag NFs-SERS sensor coupled multivariate calibration

Considering growing food safety issues, hollow Au/Ag nano-flower (HAu/Ag NFs) nanosensor has been synthesized for label-free and ultrasensitive detection of chloramphenicol (CP) via integrating the surface-enhanced Raman scattering (SERS) and multivariate calibration. As the anisotropic plasmonic nanomaterials, HAu/Ag NFs had numerous nano-chink on their surface, which offered huge hotspots for analytes. CP generated a strong SERS signal while adsorbed on the surface of HAu/Ag NFs and noted excellent linearity with 1st derivative-competitive adaptive reweighted sampling-partial least squares (CARS-PLS) in the range of 0.0001-1000 mu g/mL among the four applied multivariate calibrations. Additionally, CARS-PLS generated the lowest prediction error (RMSEP) of 0.089 and 0.123 mu g/mL for milk and water samples, respectively, and any CARS-PLS model could be used for both samples according to T-test results (P > 0.05). The intra-and interday recovery for both samples were in the range of 92.62-96.74% with CV < 10%, suggested the proposed method has excellent accuracy and precision.