School of Life Sciences and Medicine, Shandong University of Technology, Zibo, Shandong 255049, China.
School of Life Sciences and Medicine, Shandong University of Technology, Zibo, Shandong 255049, China.
Food Res Int. 2022 Oct;160:111689. doi: 10.1016/j.foodres.2022.111689. Epub 2022 Jul 14.
The classification of tea products is nowadays mainly determined by sensory assessment and chemical analysis methods. These methods are subjective, time-consuming, and laborious. In this work, a rapid analytical method for tea classification was proposed on the basis of X-ray photoelectron spectroscopy (XPS) and quantum chemical calculation. A total of 56 kinds of tea products were studied. By utilizing the data fusion strategy, the correlation between XPS peak parameters and tea characteristics was established. The quantum chemical calculations of the core-level ionization potentials deepen the understanding of the XPS features. The binding energy of the main fitted peak for O 1s, BE1, was found to have a good correlation with tea polyphenols contents, which can be used to classify tea products into six tea types (black, dark, green, oolong, white, and yellow tea) with an accuracy greater than 90 %. The results suggest that the proposed XPS method is suitable for the rapid discrimination of tea classification, which contributes to the efficient application of tea.
目前，茶叶产品的分类主要取决于感官评估和化学分析方法。这些方法主观性强、耗时耗力。在这项工作中，我们基于 X 射线光电子能谱（XPS）和量子化学计算提出了一种快速的茶叶分类分析方法。共研究了 56 种茶叶产品。通过利用数据融合策略，建立了 XPS 峰参数与茶叶特征之间的相关性。核心层电离势的量子化学计算加深了对 XPS 特征的理解。发现 O 1s 主拟合峰的结合能 BE1 与茶多酚含量有很好的相关性，可用于将茶叶产品分为黑茶、红茶、绿茶、乌龙茶、白茶和黄茶六大茶类，准确率大于 90%。结果表明，所提出的 XPS 方法适用于茶叶分类的快速鉴别，有助于茶叶的高效应用。
