Abstract
A rapid and sensitive surface-enhanced Raman spectroscopy (SERS) method combined with partial least squares (PLS) and linear regression models were developed for detecting etomidate in aquatic products. This study compared the performance of three nanoparticle substrates: silver nanoparticles (AgNPs), gold nanoparticles (AuNPs), and gold-core silver-shell nanoparticles (Au@AgNPs), with Au@AgNPs showing the highest enhancement factor (EF) of 2231, a limit of detection (LOD) of 0.1 ng/mL, and a limit of quantification (LOQ) of 0.5 ng/mL. The optimal substrate was identified as Au@AgNPs. Furthermore, the binding conditions for etomidate were optimized. The PLS model was constructed using seven latent variables (LVs), used first derivative (FD) + straight-line subtraction (SLS) preprocessing, and a spectral region of 955–1710 cm~(−1) , with R~2 C of 0.9831 and R2 P of 0.9517. The SERS method was validated in real samples, showing high accuracy and sensitivity, and a lower detection limit than HPLC. This method is valuable for ensuring seafood safety.
NETL
NSTL
Wiley