人发微量元素与性别关系的模式识别分类研究

Classification Study by Pattern Recognition on the Relationship Between the Trace Elements in Human Hair and Sex

通过对人发样品中２２种元素含量的数据进行变量扩维及压缩筛选处理，选出了影响性别判断较显著的变量，用ＰＬＳ法处理这些变量组成的数据，得到男性与女性分类清晰的二维判别图及预报模型，并根据所建立的预报模型及人发微量元素的含量判别人的性别，准确率为８１％．

The data of 22 trace elements concentrations in human hair samples were obtained by ICP AES and GFAAS. The variables which have significant influence on discriminating the sex are selected through the treatment of the concentration data by the variable dimension expansion and the variable selection methods. The discrimination plane figure with the good classification is obtained through the treatment of the data with selected variables by PLS method. The prediction models are built and used to distinguish the human sex according to the element concentrations data in human hair. The accuracy of the prediction is 81%.