Aquino Amanda J, Pereira-Filho Edenir R, Oliveira Regina V, Cass Quezia B
Separare-Núcleo de Pesquisa em Cromatografia, Departamento de Química, Universidade Federal de São Carlos, São Carlos, Brazil.
Grupo de Análise Instrumental Aplicada (GAIA), Departamento de Química, Universidade Federal de São Carlos, São Carlos, Brazil.
Front Chem. 2022 May 23;10:800729. doi: 10.3389/fchem.2022.800729. eCollection 2022.
The extensive use of medicinal herbs to traditionally treat disease persists for generations, and scientific evidence on plant-derived extracts has indicated their numerous biological activities. The , popular known as cow's paw ("pata de vaca"), with more than 60 native species, are extensively used in Brazilian popular medicine for the control of diabetes. Therefore, in 2009, , and/or were included in the Brazilian National List of Medicinal Plants of Interest to SUS (RENISUS - Brazil). In this context, this work reports the results of the chemical differentiation of , , , and using liquid chromatography coupled to high-resolution mass spectrometry and unsupervised chemometric tools. Chromatographic conditions were optimized by using the design of experiments (DoE) and chromatographic knowledge. Furthermore, the chemical profile of the studied species was analyzed by principal component analysis (PCA) and hierarchical cluster analysis that differentiated the four species of , and 55 compounds were also inferred by MS2 experiments, some of them for the first time in . In this manner, this work provides important information that could be used in quality control, development of new pharmaceuticals, and food products based on leaves, as well as to explain ethnomedicinal properties, pharmacological and toxicological actions.
传统上，药用植物被广泛用于治疗疾病已有数代之久，而关于植物提取物的科学证据表明它们具有多种生物活性。俗称牛爪（“pata de vaca”）的[植物名称未给出]，有60多种原生品种，在巴西传统医学中被广泛用于控制糖尿病。因此，在2009年，[植物名称未给出]和/或[植物名称未给出]被列入巴西统一卫生系统（SUS - 巴西）感兴趣的药用植物国家清单（RENISUS - 巴西）。在此背景下，本研究报告了运用液相色谱联用高分辨率质谱法及无监督化学计量学工具对[植物名称未给出]、[植物名称未给出]、[植物名称未给出]和[植物名称未给出]进行化学区分的结果。通过实验设计（DoE）和色谱知识对色谱条件进行了优化。此外，通过主成分分析（PCA）和层次聚类分析对所研究物种的化学图谱进行了分析，这两种分析区分了[植物名称未给出]的四个品种，并且通过MS2实验推断出了55种化合物，其中一些是在[植物名称未给出]中首次发现。通过这种方式，本研究提供了重要信息，可用于基于[植物名称未给出]叶子的质量控制、新药物和食品开发，以及解释民族药用特性、药理和毒理作用。
