支持向量机-微量元素分析法判别乌龙茶、红茶与绿茶

茶叶中的微量元素含量与产地土壤中的微量元素含量有关，故可用多种微量元素的分析数据结合模式识别计算判别茶叶产地和品牌，本工作根据三类茶叶中Zn,Mn,Mg,Cu,Al,Ca,Ba,K,8种元素的分析数据，用支持向量机算法判别乌龙茶，红茶或绿茶，并用留一法检验其预报能力。计算表明，支持向量机算法结果优于Fisher法和KNN法。采用福建安溪铁观音乌龙茶样品的元素分析结果作测试样品，检验本工作所得数学模型，判别结果亦与实际符合。

Support vector machine applied to differentiation of Oolong Tea and Black Tea or Green Tea

Since the content of trace elements of tea is related to the local soil composition, it seems possible to use the results of trace element analysis to differentiate special brand of commercial tea products. In this work, the contents of eight trace elements: Zn, Mn, Mg, Cu, Al, Ca, Ba and K in tea samples have been used for the differentiation of Oolong Tea from Black Tea or Green Tea. The cross validation by leav-ing-one method has been used to compare the prediction ability of support vector machine method with KNN and Fisher method. It has been found that the prediction result by support vector machine is better than that of KNN or Fisher method. Besides, the mathematical model ob-tained has been tested by a new sample of Oolong tea produced in Anxi county of Fujian province, and the computerized prediction result is in agreement with the fact.