Multivariate concentration determination using principal component regression with residual analysis

被引：185

作者：

Keithley, Richard B. ^{[1
]}

Heien, Michael L. ^{[2
]}

Wightman, R. Mark ^{[1
]}

机构：

[1] Univ N Carolina, Dept Chem, Chapel Hill, NC 27599 USA

[2] Penn State Univ, Dept Chem, University Pk, PA 16802 USA

来源：

TRAC-TRENDS IN ANALYTICAL CHEMISTRY | 2009年 / 28卷 / 09期

关键词：

Chemometrics; Concentration; Determination; Data analysis; Multivariate data analysis; Partial least squares (PLS); Principal component analysis (PCA); Principal component regression (PCR); Quality control; Residual analysis; LEAST-SQUARES REGRESSION; SCAN CYCLIC VOLTAMMETRY; CHEMOMETRICS; CALIBRATION;

D O I：

10.1016/j.trac.2009.07.002

中图分类号：

O65 [分析化学];

学科分类号：

070302 [分析化学];

摘要：

Data analysis is an essential tenet of analytical chemistry, extending the possible information obtained from the measurement of chemical phenomena. Chemometric methods have grown considerably in recent years, but their wide use is hindered because some still consider them too complicated. The purpose of this review is to describe a multivariate chemometric method, principal component regression, in a simple manner from the point of view of an analytical chemist, to demonstrate the need for proper quality-control (QC) measures in multivariate analysis and to advocate the use of residuals as a proper QC method. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：1127 / 1136

页数：10

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