Research on residue detection of prohibited drugs in shrimp based on the thin-layer chromatography-surface-enhanced...

In recent years, the detection of prohibited drug residues in seafood has become a critical aspect of ensuring food safety and public health. This study presents a novel analytical method combining thin-layer chromatography (TLC) and surface-enhanced Raman spectroscopy (SERS) for the detection of chloramphenicol (CAP) and malachite green (MG) in shrimp samples. Both substances are subject to strict regulation in China due to their adverse health effects and potential carcinogenic risks. Theoretical computations were performed using density functional theory to obtain the Raman and SERS spectra of CAP and MG. This enabled the extraction of their characteristic peaks in experimentally obtained TLC-SRES spectra and the explanation of the frequency shifts and selective enhancement effects of the Raman spectra that may occur under SERS conditions. The optimised TLC conditions were found to effectively separate the target compounds from complex sample matrix backgrounds, with the use of chloroform-methanol-water and ethyl acetate-anhydrous ethanol-water-ammonium hydroxide as mobile phases. This resulted in successful separation with retention factors R_f of 0.63 and 0.66, respectively. Subsequent SERS measurements achieved detection limits of 0.05 μg · kg^-1 for CAP and 0.47 μg · kg^-1 for MG in shrimp tissue. A machine learning approach that combined principal component analysis with support vector regression was developed for quantification of the residues from their TLC-SERS spectra. The quantitative models for CAP and MG in spiked shrimp samples demonstrated outstanding performance with high R² values of 0.9673 and 0.9847, and low root mean square error of prediction (RMSEP) values of 4.3802 and 5.4271, respectively. The findings demonstrated the effectiveness of the TLC-SERS method for rapid, sensitive and accurate detection of prohibited drug residues in seafood, with significant implications for food safety monitoring.