Faculty of Pharmacy, Department of Pharmacognosy, Ain Shams University, 11566, Abbassia, Cairo, Egypt.
Medicinal Chemistry and Natural Products Research Group, School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool, L3 3AF, United Kingdom.
J Pharm Biomed Anal. 2019 Feb 5;164:653-658. doi: 10.1016/j.jpba.2018.11.036. Epub 2018 Nov 15.
Green tea is a popular beverage consumed worldwide. Its quality should be controlled adequately as the quality is influenced by several factors in addition to adulterations. This study aimed to develop a simple method for assessing the quality of green tea samples obtained from the South and the East Asian regions. The UV-vis, FTIR and HPLC data from 38 samples were subjected to multivariate analyses using the unsupervised recognition techniques comprising Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA). The model for their authentication was constructed and validated by applying the supervised recognition techniques as Soft Independent Modeling of Class Analogy (SIMCA) and Partial Least Square Discriminant Analysis (PLS-DA). The percentages of caffeine in the identified samples were determined using a validated HPLC assay in addition to in vitro determination of their antioxidant activity using DPPH radical-scavenging capacity assay. HCA and PCA based on UV data successfully distributed the tested samples into informative clusters. However, that obtained from visible data could only differentiate samples with respect to their powdered condition. On the contrary, PCA from FTIR and HPLC data could hardly discriminate any of the samples. The models constructed using SIMCA and PLS-DA showed a good class separation between the South and the East Asian samples. The percentages of caffeine in the identified samples and the IC in DPPH assay are greatly diverse among all the tested samples. Thus, UV spectroscopy and chemometrics have provided a simple and quick tool for the quality control of commercial green tea samples.
绿茶是一种在世界范围内广泛消费的受欢迎的饮料。除了掺假之外，其质量还受到多种因素的影响，因此应该充分控制其质量。本研究旨在开发一种简单的方法来评估来自南亚和东亚地区的绿茶样品的质量。使用非监督识别技术（包括层次聚类分析（HCA）和主成分分析（PCA））对 38 个样品的 UV-vis、FTIR 和 HPLC 数据进行了多元分析。通过应用监督识别技术（如软独立建模分类分析（SIMCA）和偏最小二乘判别分析（PLS-DA））来构建和验证其鉴定模型。使用经过验证的 HPLC 测定法确定了鉴定样品中的咖啡因百分比，此外还使用 DPPH 自由基清除能力测定法测定了其体外抗氧化活性。基于 UV 数据的 HCA 和 PCA 成功地将测试样品分配到信息丰富的聚类中。然而，基于可见数据的聚类只能区分粉末状态的样品。相反，基于 FTIR 和 HPLC 数据的 PCA 几乎无法区分任何样品。使用 SIMCA 和 PLS-DA 构建的模型在南亚和东亚样品之间显示出良好的分类分离。鉴定样品中的咖啡因百分比和 DPPH 测定中的 IC 在所有测试样品中差异很大。因此，UV 光谱学和化学计量学为商业绿茶样品的质量控制提供了一种简单快捷的工具。
