Abstract：In order to identify variety and origin of teas, a method was proposed based on mineral content and support vector machines(SVM)．The contents of Mg，Al，P，Ca，Mn，Fe，Cu，Zn and Ba were analyzed by ICPOES and were normalized.The data were collected randomly as learning samples for designing and training multielement classifier to identify tea variety and origin by SVM. The results show that classification method which is based on ″one versus one″ multiclass support vector machine has better classification ability and stronger antijamming capability than that of cluster analysis．For small samples, the tea variety and origin identification accuracy can reach 91.67%， which illuminates that the method is effective for indentifying tea variety and origin．
李清光, 李晓钟, 钟芳. 基于矿质元素含量和支持向量机的茶叶鉴别分析[J]. 江苏大学学报（自然科学版）, 2011, 32(6): 636-641.
LI Qingguang, LI Xiaozhong, ZHONG Fang. Identification of tea based on mineral content and support vector machines[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2011, 32(6): 636-641.
2011, Vol. 32 
