Tea and Chicory Extract Characterization, Classification and Authentication by non-Targeted HPLC-UV-FLD Fingerprinting and Chemometrics
2021
Pons, Josep | Bedmar, Àlex | Núñez, Nerea | Saurina, Javier | Núñez Burcio, Oscar
Tea is a widely consumed drink in the world which is susceptible to undergo adulterations to reduce manufacture costs and rise financial benefits. The development of simple analytical methodologies to assess tea authenticity, and 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 dis-criminated from the chicory ones. 100% 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, showing also, 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.
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