中文摘要：

为了提高6种食醋的鉴别正确率,引入了基于核变换的Fisher判别分析（KFDA）方法,以及基于矩阵相似性的核参数确定方法。在选取径向基函数（RBF）为核函数,并提取食醋样本电子鼻检测信号的积分值和相对稳态平均值2种特征参量的基础上,优化确定了对应于2种特征参量的RBF核参数值,分别为5.770 0和5.387 8。对比分析了Fisher判别分析（FDA）与KFDA的鉴别结果,积分值与相对稳态平均值2种特征参量的鉴别结果分别由FDA的93.3%、90.6%提高到KFDA的98.3%、98.3%,说明合适的KFDA方法可有效提高6种食醋样品的鉴别结果。

英文摘要：

In order to enhance the correct rate of identification result of six kinds of vinegars, a kernel Fisher diseriminant analysis （KFDA） method is introduced. And a measuring method of matrix similarity based on distance discrimination was presented to define the radial basis function （RBF） characteristic parameter, where RBF was selected as the kernel function. The measuring method is that, firstly, an ideal Gram matrix is defined, and the actual kernel Gram matrix is calculated by RBF; secondly, Euclidean distance can be employed to measure the degree of approximation between the actual kernel Gram matrix and the ideal Gram matrix; finally, the optimal kernel parameter can be obtained by extremal solution of the distance. When two kinds of feature vectors, whose were integral value and average value in relative steady-state, were extracted from the E-nose signals of vinegar samples, the corresponding characteristic parameters were 5. 770 0 （ integral value） and 5. 387 8 （ average value in relative steady-state）. Comparing and analyzing the results of Fisher discriminant analysis （FDA） and KFDA, their identification correct rates were respectively from 93.3% and 90.6% （FDA） up to 98.3% and 98.3% （KFDA）. This indicates that the suitable KFDA method can effectively improve the identification results of the six kinds of vinegar samples.