Fernández-Cáceres P L, Martín M J, Pablos F, González A G
Department of Analytical Chemistry, Faculty of Chemistry, University of Seville, 41012 Seville, Spain.
J Agric Food Chem. 2001 Oct;49(10):4775-9. doi: 10.1021/jf0106143.
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.
分析了包括绿茶、红茶和速溶茶在内的46个茶叶样品的金属含量。通过电感耦合等离子体发射光谱法（ICP-AES）测定了铝、钡、钙、铜、铁、钾、镁、锰、钠、锶、钛和锌的含量。钾的平均含量为15145.4毫克/千克，是含量最高的金属。钙、镁和铝的平均含量分别为4252.4、1978.2和1074.0毫克/千克。锰的平均含量为824.8毫克/千克。绿茶和红茶的金属含量没有明显差异。应用主成分分析（PCA）、线性判别分析（LDA）和人工神经网络（ANN）等模式识别方法来区分茶叶类型。线性判别分析（LDA）和人工神经网络（ANN）在茶叶品种分类中提供了最佳结果。这些化学计量方法也有助于区分亚洲茶和非洲茶，以及不同亚洲茶的地理来源。
