Multivariate concentration determination using principal component regression with residual analysis

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

doi:10.1016/j.trac.2009.07.002

Multivariate concentration determination using principal component regression with residual analysis

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

Chemistry and Biochemistry

Research output: Contribution to journal › Review article › peer-review

139 Scopus citations

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 language	English (US)
Pages (from-to)	1127-1136
Number of pages	10
Journal	TrAC - Trends in Analytical Chemistry
Volume	28
Issue number	9
DOIs	https://doi.org/10.1016/j.trac.2009.07.002
State	Published - Oct 2009

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

Access to Document

10.1016/j.trac.2009.07.002

Cite this

@article{0d8caf993f3942acbb4ac8b003227e05,

title = "Multivariate concentration determination using principal component regression with residual analysis",

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.",

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",

author = "Keithley, {Richard B.} and {Mark Wightman}, R. and Heien, {Michael L.}",

note = "Funding Information: The authors wish to thank Leslie Sombers at North Carolina State University for the use of her data. Richard Keithley is supported with a National Defense Science & Engineering Graduate Fellowship. Research in this area in the Wightman Laboratory has been supported by the National Institutes of Health (NS 15841). ",

year = "2009",

month = oct,

doi = "10.1016/j.trac.2009.07.002",

language = "English (US)",

volume = "28",

pages = "1127--1136",

journal = "TrAC - Trends in Analytical Chemistry",

issn = "0165-9936",

publisher = "Elsevier",

number = "9",

}

TY - JOUR

T1 - Multivariate concentration determination using principal component regression with residual analysis

AU - Keithley, Richard B.

AU - Mark Wightman, R.

AU - Heien, Michael L.

N1 - Funding Information: The authors wish to thank Leslie Sombers at North Carolina State University for the use of her data. Richard Keithley is supported with a National Defense Science & Engineering Graduate Fellowship. Research in this area in the Wightman Laboratory has been supported by the National Institutes of Health (NS 15841).

PY - 2009/10

Y1 - 2009/10

N2 - 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.

AB - 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.

KW - Chemometrics

KW - Concentration

KW - Data analysis

KW - Determination

KW - Multivariate data analysis

KW - Partial least squares (PLS)

KW - Principal component analysis (PCA)

KW - Principal component regression (PCR)

KW - Quality control

KW - Residual analysis

UR - http://www.scopus.com/inward/record.url?scp=70349396621&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349396621&partnerID=8YFLogxK

U2 - 10.1016/j.trac.2009.07.002

DO - 10.1016/j.trac.2009.07.002

M3 - Review article

AN - SCOPUS:70349396621

SN - 0165-9936

VL - 28

SP - 1127

EP - 1136

JO - TrAC - Trends in Analytical Chemistry

JF - TrAC - Trends in Analytical Chemistry

IS - 9

ER -

Multivariate concentration determination using principal component regression with residual analysis

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this