A Surface-Enhanced Raman Spectroscopy Platform Integrating Dual Signal Enhancement and Machine Learning for Rapid Detection of Veterinary Drug Residues in Meat Products.
Journal:
ACS applied materials & interfaces
PMID:
40025671
Abstract
The detection and quantification of veterinary drug residues in meat remain a significant challenge due to the complex background interference inherent to the meat matrix, which compromises the stability and accuracy of spectroscopic analysis. This study introduces an advanced label-free surface-enhanced Raman spectroscopy (SERS) platform for the precise identification and quantification of veterinary drugs. By employing a dual enhancement strategy involving sodium borohydride activation and calcium ion-deuterium oxide guidance, this platform achieves the efficient capture and signal amplification of drug molecules on highly active nanoparticles. High-quality SERS spectra were obtained for carprofen, doxycycline hydrochloride, chloramphenicol, and penicillin G sodium salt, enabling accurate classification and interference suppression. In addition, the application of machine learning algorithms, including PCA-LDA, heatmap, and decision tree modeling, allows for accurate differentiation of mixed drug samples. Quantitative analyses in meat samples were achieved through Raman intensity ratios and multivariate curve resolution-alternate least-squares (MCR-ALS) analysis, with results showing high consistency with high-performance liquid chromatography (HPLC) measurements. These findings highlight the potential of this SERS-based platform for accurate and rapid detection of multicomponent veterinary drug residues in complex food matrices.