College of Life Sciences, College of Chemistry and Environmental Engineering, College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, China.
College of Life Sciences, College of Chemistry and Environmental Engineering, College of Horticulture and Gardening, Yangtze University, Jingzhou 434025, China.
Food Chem. 2024 Mar 15;436:137768. doi: 10.1016/j.foodchem.2023.137768. Epub 2023 Oct 14.
The accurate identification of tea grade is crucial to the quality control of tea. However, existing methods lack sufficient generalization ability in identifying tea grades due to the effect of temporal and spatial factors. In this study, we analyzed the effect of cultivar and altitude on EnshiYulu (ESYL) tea grades and established a robust model to evaluate their quality. Principal component analysis (PCA) revealed that differences in variety and elevation can mask grade differences. Orthogonal projection to latent structure-discriminant analysis (OPLS-DA) was used for grade identification of samples from different altitudes. For ESYL tea samples above and below 800 m altitude, 75 and 35 grade differentiated metabolites were discovered, with 14 common differentiated metabolites. Based on reconstructed OPLS-DA models, the grades of multi-altitude sources ESYL were discriminated with a rate > 85%. These results demonstrate the potential of a grade discrimination model based on common differential metabolites, which exhibits generalization ability.
准确识别茶叶等级对于茶叶的质量控制至关重要。然而，由于时间和空间因素的影响，现有的方法在识别茶叶等级方面缺乏足够的泛化能力。在本研究中，我们分析了品种和海拔高度对恩施玉露（ESYL）茶叶等级的影响，并建立了一个强大的模型来评估其质量。主成分分析（PCA）表明，品种和海拔的差异可以掩盖等级差异。正交投影到潜在结构判别分析（OPLS-DA）用于不同海拔高度的样本的等级识别。对于海拔 800m 以上和以下的 ESYL 茶叶样品，发现了 75 种和 35 种有差异的代谢产物，其中有 14 种共同的差异代谢产物。基于重建的 OPLS-DA 模型，对多海拔来源的 ESYL 等级进行了区分，准确率>85%。这些结果表明，基于共同差异代谢物的等级判别模型具有潜在的通用性。
