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Establishment of the thin-layer chromatography-surface-enhanced Raman spectroscopy and chemometrics method for simultaneous identification of eleven illegal drugs in anti-rheumatic health food

化学计量学 色谱法 化学 表面增强拉曼光谱 毒品检测 吡罗昔康 薄层色谱法 萘普生 检出限 线性判别分析 拉曼光谱 人工智能 物理 光学 医学 替代医学 病理 拉曼散射 计算机科学
作者
Fangwei Yang,Cheng Wang,Hang Yu,Yahui Guo,Yuliang Cheng,Weirong Yao,Yunfei Xie
出处
期刊:Food bioscience [Elsevier]
卷期号:49: 101842-101842 被引量:18
标识
DOI:10.1016/j.fbio.2022.101842
摘要

The risk of the illegal addition of anti-inflammatory and analgesic chemical drugs in anti-rheumatic health foods should not be ignored. Market supervision and rapid on-site detection technology need to be strengthened. Thin-layer chromatography-surface-enhanced Raman spectroscopy (TLC-SERS), which has the advantages of simple operation, fast separation, and qualitative and quantitative detection, was used in this study. And these eleven chemical drugs (acetaminophen, acetylsalicylic acid, aminophenazone, dexamethasone, diclofenac sodium, hydrocortisone, indometacin, naproxen, phenylbutazone, piroxicam, prednisone 21-acetate) that may be added to anti-rheumatic health foods have been simultaneously identified by TLC-SERS combined with chemometrics method. The characteristic signals of the separated drug spots were collected by SERS, which were optimized by the gold colloidal nanoparticles' volume and integration time. Then SERS was subjected to principal component analysis (PCA) to reduce dimensionality and combined with the pattern recognition methods in chemometrics, such as PCA-Linear Discriminant Analysis (LDA), PCA-K Nearest Neighbor and PCA-Support Vector Machine, and eleven drug components were judged and identified. Moreover, the predictive performances of different models were also analyzed and compared. The results showed that the TLC plate and four organic solvents of petroleum ether, chloroform, ethyl acetate and acetic acid were selected as the developing agent. The dropping amount of gold colloidal nanoparticles and the integration time were set and optimized. The limit of detection of the simultaneous detection method of SERS was 10–100 mg/L. Furthermore, SERS was preprocessed by Gap-Segment 2nd Derivative, and then the PCA-LDA model was established, and the model's prediction accuracy can reach 100%. The method is simple, rapid, sensitive and accurate, and has low experimental instruments and equipment requirements. It is suitable for the on-site simultaneous detection of various anti-inflammatory and analgesic chemical drugs in health foods. It can also provide guarantee and support for the establishment of appropriate rapid detection methods and the development of supervision technology.
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