College of Resource and Environmental Engineering, Guizhou University, Guiyang 550025, China.
College of Mining, Guizhou University, Guiyang 550025, China.
Molecules. 2018 Nov 18;23(11):3013. doi: 10.3390/molecules23113013.
This study aimed to construct objective and accurate geographical discriminant models for tea leaves based on multielement concentrations in combination with chemometrics tools. Forty mineral elements in 87 tea samples from three growing regions in Guizhou Province (China), namely Meitan and Fenggang (MTFG), Anshun (AS) and Leishan (LS) were analyzed. Chemometrics evaluations were conducted using a one-way analysis of variance (ANOVA), principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that the concentrations of the 28 elements were significantly different among the three regions ( < 0.05). The correct classification rates for the 87 tea samples were 98.9% for LDA and 100% for OPLS-DA. The variable importance in the projection (VIP) values ranged between 1.01⁻1.73 for 11 elements (Sb, Pb, K, As, S, Bi, U, P, Ca, Na, and Cr), which can be used as important indicators for geographical origin identification of tea samples. In conclusion, multielement analysis coupled with chemometrics can be useful for geographical origin identification of tea leaves.
本研究旨在构建基于多元素浓度结合化学计量学工具的茶叶客观准确的地理判别模型。对来自中国贵州省三个种植区（湄潭和凤岗（MTFG）、安顺（AS）和雷山（LS））的 87 个茶叶样本中的 40 种矿物质元素进行了分析。采用单因素方差分析（ANOVA）、主成分分析（PCA）、线性判别分析（LDA）和正交偏最小二乘判别分析（OPLS-DA）对化学计量学评价进行了分析。结果表明，三个地区之间（<0.05）28 种元素的浓度存在显著差异。LDA 对 87 个茶叶样本的正确分类率为 98.9%，OPLS-DA 的正确分类率为 100%。11 种元素（Sb、Pb、K、As、S、Bi、U、P、Ca、Na 和 Cr）的变量重要性投影（VIP）值在 1.01-1.73 之间，可作为茶叶样品地理来源鉴定的重要指标。总之，多元分析结合化学计量学可用于茶叶的地理来源鉴定。
