基于矿质元素含量和支持向量机的茶叶鉴别分析  

Identification of tea based on mineral content and support vector machines

作　　者：李清光[1] 李晓钟 钟芳[1] 

机构地区：[1]江南大学食品学院,江苏无锡214122 [2]江南大学商学院,江苏无锡214122

出　　处：《江苏大学学报（自然科学版）》2011年第6期636-641,共6页Journal of Jiangsu University：Natural Science Edition

基　　金：教育部人文社会科学研究规划基金资助项目(10YJA790098);江苏省普通高校研究生科研创新计划项目(CX10B_232Z);江南大学博士研究生科学研究基金资助项目(JUDCF10010)

摘　　要：为了实现茶叶种类与产地的识别,提出了一种基于矿质元素和支持向量机的茶叶鉴别方法.该方法首先运用ICP光谱仪测定30个茶叶样本中的Mg,Al,P,Ca,Mn,Fe,Cu,Zn,Ba等共16种元素含量,接着对采集到的数据进行标准化处理,随机抽取样本用于设计训练基于支持向量机的多元分类器,然后对测试样本进行种类与产地识别.试验结果表明,采用"一对一"的多分类支持向量机方法比聚类分析具有更好的抗干扰性和更强的分类能力,在小样本的情况下对茶叶种类和产地的识别率均达到91.67%,能有效进行茶叶鉴别.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 ICP-OES 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″ multi-class support vector machine has better classification ability and stronger anti-jamming 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.

关 键 词：茶叶 矿质元素 支持向量机 鉴别 产地 种类 

分 类 号：TS272.7[农业科学—茶叶生产加工]