Multivariate concentration determination using principal component regression with residual analysis

Richard B. Keithley, R. Mark Wightman, Michael L Heien

Research output: Contribution to journalArticle

106 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1127-1136
Number of pages10
JournalTrends in Analytical Chemistry
Volume28
Issue number9
DOIs
StatePublished - Oct 2009
Externally publishedYes

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Quality control
quality control
chemical phenomena
multivariate analysis
Chemical analysis
method
analysis
Multivariate Analysis
control measure
data analysis
analytical chemistry
need

Keywords

  • Chemometrics
  • Concentration
  • Data analysis
  • Determination
  • Multivariate data analysis
  • Partial least squares (PLS)
  • Principal component analysis (PCA)
  • Principal component regression (PCR)
  • Quality control
  • Residual analysis

ASJC Scopus subject areas

  • Analytical Chemistry
  • Spectroscopy
  • Environmental Chemistry

Cite this

Multivariate concentration determination using principal component regression with residual analysis. / Keithley, Richard B.; Mark Wightman, R.; Heien, Michael L.

In: Trends in Analytical Chemistry, Vol. 28, No. 9, 10.2009, p. 1127-1136.

Research output: Contribution to journalArticle

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