Scientific Direction Chemical and Physical Health Risks, Section Medicines & Health Products, Sciensano, J. Wytsmanstraat 14, B-1050, Brussels, Belgium.
Scientific Direction Chemical and Physical Health Risks, Section Medicines & Health Products, Sciensano, J. Wytsmanstraat 14, B-1050, Brussels, Belgium.
J Pharm Biomed Anal. 2019 Mar 20;166:189-196. doi: 10.1016/j.jpba.2019.01.015. Epub 2019 Jan 11.
The sale and consumption of plant food supplements is increasing, especially in the western world. A lot of these supplements can be bought through internet, where a lot of illegal trade is going on. Every year seized dietary supplements are send to laboratories in order to screen for the presence of chemical adulterants or illegally added active pharmaceutical ingredients, though also herbal adulteration occurs and is given less attention. In this paper a two-step approach is presented based on fingerprints recorded by both infrared spectroscopy as liquid chromatography with UV-detection for the screening of five regulated plants used in respectively dietary supplements for slimming and potency enhancement. Both types of fingerprints are combined with chemometric techniques in order to obtain classification models. A first classification model is calculated based on the infrared data and gives a first idea about the plant suspected to be present. This suspicion is then confirmed based on binary classification models calculated with the chromatographic data obtained for the suspected plant. In general, good classification models were obtained for each of the targeted plants. The approach was applied in a small market study comprising 35 dietary supplements for slimming and 34 for male potency enhancement. In total 21 samples were found to contain one of the five targeted plants.
植物性食品补充剂的销售和消费正在增加,特别是在西方世界。许多这些补充剂可以通过互联网购买,大量的非法交易正在进行。每年都有被扣押的膳食补充剂被送到实验室,以筛选是否存在化学掺杂物或非法添加的活性药物成分,尽管也会发生草药掺假,但却没有得到太多关注。在本文中,提出了一种两步法,基于红外光谱和液相色谱与紫外检测记录的指纹图谱,用于筛查分别用于减肥和增强效力的膳食补充剂中的五种受监管植物。将这两种类型的指纹图谱与化学计量学技术相结合,以获得分类模型。首先根据红外数据计算分类模型,对疑似存在的植物有一个初步的了解。然后根据对疑似植物获得的色谱数据计算的二进制分类模型来确认这种怀疑。总的来说,针对每个目标植物都获得了良好的分类模型。该方法应用于一个小型市场研究,包括 35 种减肥膳食补充剂和 34 种增强男性效力的膳食补充剂。共有 21 个样本被发现含有五种目标植物中的一种。