The use and misuse of chemometrics for treating classification problems

被引:149
作者
Defernez, M
Kemsley, EK
机构
[1] Institute of Food Research, Colney, Norwich, NR4 7UA, Norwich Research Park
关键词
D O I
10.1016/S0165-9936(97)00015-0
中图分类号
O65 [分析化学];
学科分类号
070302 [分析化学]; 081704 [应用化学];
摘要
In this article, we examine the increasing use by analytical chemists of chemometric methods for treating classification problems. The methods considered are principal component analysis (PCA), canonical variates analysis (CVA), discriminant analysis (DA), and discriminant partial least squares (PLS). Overfitting, a potential hazard of multivariate modelling, is illustrated using examples of real and simulated data, and the importance of model validation is discussed.
引用
收藏
页码:216 / 221
页数:6
相关论文
共 5 条
[1]
[Anonymous], 1988, PRINCIPLES MULTIVARI
[2]
Joliffe I.T., 1986, Principal Component Analysis
[3]
Discriminant analysis of high-dimensional data: A comparison of principal components analysis and partial least squares data reduction methods [J].
Kemsley, EK .
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1996, 33 (01) :47-61
[4]
KRZANOWSKI WJ, 1988, PRINCIPLES MULTIVARI, P53
[5]
Mark H. L., 1985, ANAL CHEM, V57, P1449, DOI DOI 10.1021/AC00284A061