High-throughput Flow Injection Analysis-Mass Spectrometry (FIA-MS) Fingerprinting for the Authentication of Tea. Application to the Detection of Teas Adulterated with Chicory.

Abstract

Tea is a broadly consumed beverage worldwide that is susceptible to fraudulent practices, in-cluding its adulteration with other plants such as chicory extracts. In the present work, a non-targeted high-throughput flow injection analysis-mass spectrometry (FIA-MS) fingerprint-ing methodology was employed to characterize and classify different varieties of tea (black, green, red, oolong, and white) and chicory extracts by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Detection and quantitation of frauds in black and green tea extracts adulterated with chicory were also evaluated as proofs of concept using partial least squares (PLS) regression. Overall, PLS-DA showed that FIA-MS fingerprints in both negative and positive ionization modes were excellent sample chemical descriptors to dis-criminate tea samples from chicory independently of the tea product variety, as well as to clas-sify and discriminate among some of the analyzed tea groups. The classification rate was 100% in all the paired cases ¿i.e., each tea product variety versus chicory¿ by PLS-DA calibration and prediction models showing their capability to assess tea authentication. The results obtained for chicory adulteration detection and quantitation using PLS were satisfactory in the two adultera-tion cases evaluated (green and black teas adulterated with chicory), with calibration, cross-validation, and prediction errors bellow 5.8%, 8.5%, and 16.4%, respectively. Thus, the non-targeted FIA-MS fingerprinting methodology demonstrated to be a high-throughput, cost-effective, simple, and reliable approach to assess tea authentication issues.