Multivariate concentration determination using principal component regression with residual analysis

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 purpo...

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Bibliographic Details
Published in:TrAC, Trends in analytical chemistry (Regular ed.) Trends in analytical chemistry (Regular ed.), 2009-10, Vol.28 (9), p.1127-1136
Main Authors: Keithley, Richard B., Mark Wightman, R., Heien, Michael L.
Format: Article
Language:English
Subjects:
Analytical chemistry
Chemistry
Chemometrics
Concentration
Data analysis
Determination
Exact sciences and technology
Multivariate data analysis
Partial least squares (PLS)
Principal component analysis (PCA)
Principal component regression (PCR)
Quality control
Residual analysis
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Summary: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.
ISSN:0165-9936
1879-3142
DOI:10.1016/j.trac.2009.07.002