Near infrared (NIR) spectroscopy-based classification for the authentication of Darjeeling black tea. (June 2019)

• Record Type: Journal Article
• Authors: Firmani, Patrizia; De Luca, Silvia; Bucci, Remo; Marini, Federico; Biancolillo, Alessandra
• Is Part Of: Food control. Volume 100(2019)
• Page Start: 292
• Page End: 299
• Publication Date: 2019-06
• Subjects: Darjeeling -- Tea -- Classification -- PLS-DA -- SIMCA -- NIR spectroscopy; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2019.02.006
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Darjeeling black tea is a worldwide known tea variety which is currently part of the register of protected designations of origin (PDO) and protected geographical indications (PGI) as established by Commission Implementing Regulation (EU) No 1050/2011 of 20 October 2011. Therefore, preventing frauds against this product became increasingly important in order to protect producers and consumers from possible economic losses. Starting from this assumption, the present work aims at two different goals: the first one is to develop a rapid, non-destructive and relatively cheap method to distinguish PGI Darjeeling tea from other kinds of black teas, and the second one is to test a non-invasive approach suitable to detect adulterated Darjeeing tea samples. To achieve this goals, NIR spectroscopy has been coupled with two different classifiers: Partial Least Squares-Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogies (SIMCA). Both provided satisfactory results in discriminating PGI samples from the other teas and from the adulterated Darjeeling. Highlights: Authentication of PGI Darjeeling tea by rapid and non-invasive approach (NIR + chemometrics). Discrimination of PGI Darjeeling tea from other black teas. Indentification of possible addition of other tea leaves to Darjeeling tea. Partial least squares discriminant analysis performed better than soft independent modeling of class analogies.