Department of Pharmaceutical Chemistry, Riga Stradiņš University, 16 Dzirciema Str., LV-1007 Riga, Latvia.
Baltic Biomaterials Centre of Excellence, Riga Technical University, 1 Kalku Str., LV-1658 Riga, Latvia.
Molecules. 2022 Apr 15;27(8):2555. doi: 10.3390/molecules27082555.
The growing market of herbal medicines, the increase in international trade in Latvia, and the lack of adequate analytical methods have raised the question of the potential use of herbal fingerprinting methods. In this study, high-performance liquid chromatography (HPLC) and thin layer chromatography (TLC) methods were developed for obtaining chromatographic fingerprints of four taxonomically and evolutionary different medicinal plants ( L., L., L., L.). Retention time shifting, principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal projections to latent structures (OPLS) analysis were used to improve and analyze the obtained fingerprints. HPLC data detection at 270 nm was determined superior to 360 nm for the distinction of medicinal plants and used data alignment method significantly increased similarity between samples. Analyzed medicinal plant extracts formed separate, compact clusters in PCA, and the results of HCA correlated with the evolutionary relationships of the analyzed medicinal plants. Herbal fingerprinting using chromatographic analysis coupled with multivariate analysis has a great potential for the identification of medicinal plants as well as for the distinction of Latvian native medicinal plants.
草药市场的不断增长、拉脱维亚国际贸易的增加以及缺乏足够的分析方法，这些都使得人们开始考虑潜在地使用草药指纹图谱方法。在本研究中，开发了高效液相色谱（HPLC）和薄层色谱（TLC）方法，以获得四种在分类学和进化上不同的药用植物（ 、 、 、 ）的色谱指纹图谱。保留时间偏移、主成分分析（PCA）、层次聚类分析（HCA）和正交投影到潜在结构（OPLS）分析被用于改进和分析获得的指纹图谱。在区分药用植物方面，HPLC 在 270nm 处的数据检测优于 360nm，而使用数据对齐方法则显著提高了样品之间的相似度。分析的药用植物提取物在 PCA 中形成了单独的、紧凑的聚类，HCA 的结果与分析的药用植物的进化关系相关。使用结合了多变量分析的色谱分析进行草药指纹图谱分析，对于识别药用植物以及区分拉脱维亚本土药用植物具有很大的潜力。
