Ascorbate functionalized Au@AgNPs SERS sensor combined random frog-partial least squares for the prediction of chloramphenicol in milk

Abstract

The residue of chloramphenicol (CAP) in milk is a great threat to human health. Therefore, the study designed a simple ultrasensitive label-free surface-enhanced Raman scattering (SERS) sensing system for milk employing ascorbate functionalized core-shell gold-silver nanoparticles (Au@AgNPs) synthesized with 2.1 of 10 mM AgNO3 coupled chemometric method. Au@AgNPs with an enhancement factor of 6.99 × 106 was applied to acquire the SERS spectrum of CAP and showed linearity in the range of 0.0001–1000 µg/mL with 1st derivative random frog-partial least squares (RF-PLS) model. Three chemometric models were comparatively applied, among them, the RF-PLS model showed the best results and achieved a correlation coefficient in calibration = 0.985 and prediction = 0.9783 with prediction error (RMSEP) = 0.475 µg/mL with a limit of detection of 2.73 × 10−5 µg/mL. Moreover, recovery of intra- and inter-day analysis was 88–94.38% with a relative standard deviation (RPD) of < 10%. Furthermore, high-performance liquid chromatography (HPLC) analysis was conducted to compare the results with the developed method, and an insignificant (p > 0.05) difference was observed, indicating that the constructed method has good accuracy for the prediction of CAP in milk.