Differentiation of tea (Camellia sinensis) varieties and their geographical origin according to their metal content.

The metal content of 46 tea samples, including green, black, and instant teas, was analyzed. Al, Ba, Ca, Cu, Fe, K, Mg, Mn, Na, Sr, Ti, and Zn were determined by ICP-AES. Potassium, with an average content of 15145.4 mg kg(-1) was the metal with major content. Calcium, magnesium, and aluminum had average contents of 4252.4, 1978.2, and 1074.0 mg kg(-1), respectively. The average amount of manganese was 824.8 mg kg(-1). There were no clear differences between the metal contents of green and black teas. Pattern recognition methods such as principal component analysis (PCA), linear discriminant analysis (LDA), and artificial neural networks (ANN), were applied to differentiate the tea types. LDA and ANN provided the best results in the classification of tea varieties. These chemometric procedures were also useful for distinguishing between Asian and African teas and between the geographical origin of different Asian teas.