Tan Ailing, He Yunhao, Wang Haoyu, Zhang Zixuan, Zhao Rongxuan, Ma Wei, Zhao Yong
The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information and Science Engineering, Yanshan University, Qinhuangdao, China.
The Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, China.
Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 2025 Jul;42(7):925-939. doi: 10.1080/19440049.2025.2512879. Epub 2025 Jun 9.
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 of 0.63 and 0.66, respectively. Subsequent SERS measurements achieved detection limits of 0.05 μg · kg for CAP and 0.47 μg · kg 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 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.
近年来，检测海鲜中违禁药物残留已成为确保食品安全和公众健康的关键环节。本研究提出了一种将薄层色谱法（TLC）与表面增强拉曼光谱法（SERS）相结合的新型分析方法，用于检测虾类样品中的氯霉素（CAP）和孔雀石绿（MG）。由于这两种物质对健康有不良影响且存在潜在致癌风险，在中国受到严格监管。利用密度泛函理论进行了理论计算，以获得CAP和MG的拉曼光谱和SERS光谱。这使得能够在实验获得的TLC-SRES光谱中提取它们的特征峰，并解释在SERS条件下可能出现的拉曼光谱的频移和选择性增强效应。发现优化后的TLC条件能够有效地将目标化合物从复杂的样品基质背景中分离出来，使用氯仿-甲醇-水和乙酸乙酯-无水乙醇-水-氢氧化铵作为流动相。分别以保留因子0.63和0.66成功实现分离。随后的SERS测量在虾组织中对CAP的检测限为0.05μg·kg，对MG的检测限为0.47μg·kg。开发了一种将主成分分析与支持向量回归相结合的机器学习方法，用于从其TLC-SERS光谱中对残留物进行定量。加标虾样品中CAP和MG的定量模型表现出色，R²值分别为0.9673和0.9847，预测均方根误差（RMSEP）值分别为4.3802和5.4271。研究结果证明了TLC-SERS方法在快速、灵敏和准确检测海鲜中违禁药物残留方面的有效性，对食品安全监测具有重要意义。
