RECENT DEVELOPMENTS IN MULTIVARIATE CALIBRATION

被引:124
作者
KOWALSKI, BR
SEASHOLTZ, MB
机构
关键词
MULTIVARIATE CALIBRATION; BIASED REGRESSION; PARTIAL LEAST SQUARES (PLS); PRINCIPAL COMPONENT REGRESSION (PCR); MODEL VALIDATION; NONLINEAR CALIBRATION;
D O I
10.1002/cem.1180050303
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
With the goal of understanding global chemical processes, environmental chemists have some of the most complex sample analysis problems. Multivariate calibration is a tool that can be applied successfully in many situations where traditional univariate analyses cannot. The purpose of this paper is to review multivariate calibration, with an emphasis being placed on the developments in recent years. The inverse and classical models are discussed briefly, with the main emphasis on the biased calibration methods. Principal component regression (PCR) and partial least squares (PLS) are discussed, along with methods for quantitative and qualitative validation of the calibration models. Non-linear PCR, non-linear PLS and locally weighted regression are presented as calibration methods for non-linear data. Finally, calibration techniques using a matrix of data per sample (second-order calibration) are discussed briefly.
引用
收藏
页码:129 / 145
页数:17
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