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基于非靶向HPLC-UV-FLD指纹图谱和化学计量学的茶叶和菊苣提取物表征、分类及鉴别

Tea and Chicory Extract Characterization, Classification and Authentication by Non-Targeted HPLC-UV-FLD Fingerprinting and Chemometrics.

作者信息

Pons Josep, Bedmar Àlex, Núñez Nerea, Saurina Javier, Núñez Oscar

机构信息

Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, E08028 Barcelona, Spain.

Research Institute in Food Nutrition and Food Safety, University of Barcelona, Recinte Torribera, Av. Prat de la Riba 171, Edifici de Recerca (Gaudí), Santa Coloma de Gramenet, E08921 Barcelona, Spain.

出版信息

Foods. 2021 Nov 28;10(12):2935. doi: 10.3390/foods10122935.

Abstract

Tea is a widely consumed drink in the world which is susceptible to undergoing adulterations to reduce manufacturing costs and rise financial benefits. The development of simple analytical methodologies to assess tea authenticity, as well as to detect and quantify frauds, is an important matter considering the rise of adulteration issues in recent years. In the present study, untargeted HPLC-UV and HPLC-FLD fingerprinting methods were employed to characterize, classify, and authenticate tea extracts belonging to different varieties (red, green, black, oolong, and white teas) by partial least squares-discriminant analysis (PLS-DA), as well as to detect and quantify adulteration frauds when chicory was used as the adulterant by partial least squares (PLS) regression, to ensure the authenticity and integrity of foodstuffs. Overall, PLS-DA showed a good classification and grouping of the tea samples according to the tea variety and, except for some white tea extracts, perfectly discriminated from the chicory ones. One hundred percent classification rates for the PLS-DA calibration models were achieved, except for green and oolong tea when HPLC-FLD fingerprints were employed, which showed classification rates of 96.43% and 95.45%, respectively. Good predictions were also accomplished, also showing, in almost all the cases, a 100% classification rate for prediction, with the exception of white tea and oolong tea when HPLC-UV fingerprints were employed that exhibited a classification rate of 77.78% and 88.89%, respectively. Good PLS results for chicory adulteration detection and quantitation were also accomplished, with calibration, cross-validation, and external validation errors beneath 1.4%, 6.4%, and 3.7%, respectively. Acceptable prediction errors (below 21.7%) were also observed, except for white tea extracts that showed higher errors which were attributed to the low sample variability available.

摘要

茶是世界上广泛消费的饮品,容易受到掺假影响,以降低制造成本并提高经济效益。考虑到近年来掺假问题的增加,开发简单的分析方法来评估茶叶的真实性,以及检测和量化欺诈行为是一个重要问题。在本研究中,采用非靶向高效液相色谱 - 紫外(HPLC - UV)和高效液相色谱 - 荧光(HPLC - FLD)指纹图谱方法,通过偏最小二乘判别分析(PLS - DA)对不同品种(红茶、绿茶、黑茶、乌龙茶和白茶)的茶叶提取物进行表征、分类和鉴定,并通过偏最小二乘(PLS)回归检测和量化当菊苣用作掺假物时的掺假欺诈行为,以确保食品的真实性和完整性。总体而言,PLS - DA根据茶叶品种对茶叶样品进行了良好的分类和分组,除了一些白茶提取物外,与菊苣提取物有完美区分。PLS - DA校准模型实现了100%的分类率,使用HPLC - FLD指纹图谱时绿茶和乌龙茶除外,其分类率分别为96.43%和95.45%。也取得了良好的预测结果,几乎在所有情况下预测的分类率也为100%,使用HPLC - UV指纹图谱时白茶和乌龙茶除外,其分类率分别为77.78%和88.89%。对于菊苣掺假检测和定量也取得了良好的PLS结果,校准、交叉验证和外部验证误差分别低于1.4%、6.4%和3.7%。除了白茶提取物显示出较高误差(归因于可用样品变异性低)外,还观察到可接受的预测误差(低于21.7%)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f815/8700607/cccb6e984bfa/foods-10-02935-g001.jpg

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