This paper (open access) reports the development of a classification model for the geographic origin on black tea based upon measuring a panel of 15 trace elements by X-ray fluorescence (XRF). XRF is a non-destructive technique. The only sample preparation required is grinding the tea leaves into a fine powder.
The model could discriminate between 10 major tea-producing regions. It was built using reference samples obtained, via tea industry contacts, directly from plantations or primary processing facilities. 791 black tea samples were collected in total: Assam (272 samples), Burundi (40 samples), Darjeeling (145 samples), Ethiopia (40 samples), Keemun (115 samples), Kenya region 1 (41 samples), Kenya region 2 (40 samples), Malawi (40 samples), Rwanda (10 samples), and Sri Lanka (48 samples).
Two unsupervised analysis techniques were used to visualize high-dimensional data, and six supervised models were employed to discriminate the ten GI regions.
The authors report that machine learning models, including random forest, support vector machine, k-nearest neighbours, linear discriminate analysis, and the deep learning multilayer perceptron (MLP) model, demonstrated superior predictive capabilities compared to the traditional partial least squares discriminant analysis model. The MLP model achieved the highest performance, with a 97.7 % overall F1 score in predicting the geographical origins of 532 authentic samples across ten GI regions.
The authors also Identified Rb, Sr, Mn, Si, and Cl as geographical markers for African region discrimination.
The conclude that their work could form the basis and foundation for an international database of tea Geographic Origin, enabling cheap and quick authenticity verification testing.
Photo by Oleg Guijinsky on Unsplash
