Henan Key Laboratory of Biomarker Based Rapid-detection Technology for Food Safety, Food and Pharmacy College, Xuchang University, Xuchang 461000, PR China.
College of Life Sciences, Yangtze University, Jingzhou 434023, China.
Food Res Int. 2023 Jan;163:112278. doi: 10.1016/j.foodres.2022.112278. Epub 2022 Nov 30.
The flavor and aroma quality of green tea are closely related to the harvest season. The aim of this study was to identify the harvesting season of green tea by alcohol/salt-based aqueous two-phase system (ATPS) combined with chemometric analysis. In this paper, the single factor experiments (SFM) and response surface methodology (RSM) optimization were designed to investigate and select the optimal ATPS. A total of 180 green tea samples were studied in this work, including 86 spring tea and 94 autumn tea. After the active components in green tea samples were extracted by the optimal ethanol/(NH)SO ATPS, the qualitative and quantitative analysis was realized based on HPLC-DAD combined with alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) algorithm, with satisfactory spiked recoveries (86.00 %-112.45 %). The quantitative results obtained from ATLD-MCR model were subjected to chemometric pattern recognition analysis. The constructed partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models showed better results than the principal component analysis (PCA) model, and the RX values (>0.835) and RY (>0.937) were close to 1, the Q values were greater than 0.75 (>0.933), and the differences between RY and Q were not larger than 0.2, indicating excellent cross-validation prediction performance of the models. Furthermore, the classification results based on the hierarchical clustering analysis (HCA) were consistent with the PCA, PLS-DA and OPLS-DA results, establishing a good correlation between tea active components and the harvesting seasons of green tea. Overall, the combination of ATPS and chemometric methods is accurate, sensitive, fast and reliable for the qualitative and quantitative determination of tea active components, providing guidance for the quality control of green tea.
绿茶的风味和香气质量与收获季节密切相关。本研究旨在通过醇/盐双水相系统（ATPS）结合化学计量学分析来鉴定绿茶的收获季节。在本文中，设计了单因素实验（SFM）和响应面法（RSM）优化来研究和选择最佳的 ATPS。共研究了 180 个绿茶样品，包括 86 个春茶和 94 个秋茶。在通过最佳乙醇/（NH）SO ATPS 提取绿茶样品中的活性成分后，基于 HPLC-DAD 结合交替三线性分解辅助多元曲线分辨（ATLD-MCR）算法实现了定性和定量分析，具有令人满意的加标回收率（86.00%-112.45%）。从 ATLD-MCR 模型获得的定量结果进行了化学计量模式识别分析。构建的偏最小二乘判别分析（PLS-DA）和正交偏最小二乘判别分析（OPLS-DA）模型优于主成分分析（PCA）模型，RX 值（>0.835）和 RY 值（>0.937）接近 1，Q 值大于 0.75（>0.933），RY 和 Q 值之间的差值不大于 0.2，表明模型具有出色的交叉验证预测性能。此外，基于层次聚类分析（HCA）的分类结果与 PCA、PLS-DA 和 OPLS-DA 结果一致，建立了绿茶活性成分与收获季节之间的良好相关性。总之，ATPS 和化学计量方法的结合对于茶活性成分的定性和定量测定是准确、灵敏、快速和可靠的，为绿茶的质量控制提供了指导。
