Two-Way Data Analysis: Detection of Purest Variables

Willem Windig; Andrey Bogomolov; Sergey Kucheryavskiy

doi:10.1016/B978-0-12-409547-2.14747-X

Two-Way Data Analysis: Detection of Purest Variables

Willem Windig, Andrey Bogomolov^*, Sergey Kucheryavskiy

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Book chapter › Research › peer-review

Abstract

This chapter is focused on approaches used for the detection of purest variables (or samples) in two-way data analysis. The concept of purity is a powerful tool for mixture analysis by spectroscopic or similar methods. Finding rows or columns of the data matrix that carry the contribution from the only component has an independent value helping to better understand the experiment, for instance, to identify mixture constituents or to estimate the quality of chromatographic separation. Detected pure variables can be used as the basis for further “spectral unmixing”—to perform full multivariate curve resolution of the data. Presented methods and algorithms are explained in detail, and their efficiency is illustrated by practical examples.

Original language	English
Title of host publication	Comprehensive Cheometircs : Chemical and Biochemical Data Analysis
Editors	Steven Brown, Romà Tauler, Beata Walczak
Number of pages	33
Volume	2
Publisher	Elsevier
Publication date	2020
Edition	2
Pages	275-307
ISBN (Electronic)	9780124095472
DOIs	https://doi.org/10.1016/B978-0-12-409547-2.14747-X
Publication status	Published - 2020

Keywords

Alternating least squares
Contrast constraint
Key set factor analysis
Mixture analysis
Spectral unmixing
Spectroscopy
Stepwise maximum angle calculation
Step-wise maximum angle calculation
Variable purity

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10.1016/B978-0-12-409547-2.14747-X

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title = "Two-Way Data Analysis: Detection of Purest Variables",

abstract = "This chapter is focused on approaches used for the detection of purest variables (or samples) in two-way data analysis. The concept of purity is a powerful tool for mixture analysis by spectroscopic or similar methods. Finding rows or columns of the data matrix that carry the contribution from the only component has an independent value helping to better understand the experiment, for instance, to identify mixture constituents or to estimate the quality of chromatographic separation. Detected pure variables can be used as the basis for further “spectral unmixing”—to perform full multivariate curve resolution of the data. Presented methods and algorithms are explained in detail, and their efficiency is illustrated by practical examples.",

keywords = "Alternating least squares, Contrast constraint, Key set factor analysis, Mixture analysis, Spectral unmixing, Spectroscopy, Stepwise maximum angle calculation, Step-wise maximum angle calculation, Variable purity",

author = "Willem Windig and Andrey Bogomolov and Sergey Kucheryavskiy",

year = "2020",

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language = "English",

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edition = "2",

}

Two-Way Data Analysis: Detection of Purest Variables. / Windig, Willem; Bogomolov, Andrey; Kucheryavskiy, Sergey.
Comprehensive Cheometircs: Chemical and Biochemical Data Analysis. ed. / Steven Brown; Romà Tauler; Beata Walczak. Vol. 2 2. ed. Elsevier, 2020. p. 275-307.

Research output: Contribution to book/anthology/report/conference proceeding › Book chapter › Research › peer-review

TY - CHAP

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AU - Windig, Willem

AU - Bogomolov, Andrey

AU - Kucheryavskiy, Sergey

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Y1 - 2020

N2 - This chapter is focused on approaches used for the detection of purest variables (or samples) in two-way data analysis. The concept of purity is a powerful tool for mixture analysis by spectroscopic or similar methods. Finding rows or columns of the data matrix that carry the contribution from the only component has an independent value helping to better understand the experiment, for instance, to identify mixture constituents or to estimate the quality of chromatographic separation. Detected pure variables can be used as the basis for further “spectral unmixing”—to perform full multivariate curve resolution of the data. Presented methods and algorithms are explained in detail, and their efficiency is illustrated by practical examples.

AB - This chapter is focused on approaches used for the detection of purest variables (or samples) in two-way data analysis. The concept of purity is a powerful tool for mixture analysis by spectroscopic or similar methods. Finding rows or columns of the data matrix that carry the contribution from the only component has an independent value helping to better understand the experiment, for instance, to identify mixture constituents or to estimate the quality of chromatographic separation. Detected pure variables can be used as the basis for further “spectral unmixing”—to perform full multivariate curve resolution of the data. Presented methods and algorithms are explained in detail, and their efficiency is illustrated by practical examples.

KW - Alternating least squares

KW - Contrast constraint

KW - Key set factor analysis

KW - Mixture analysis

KW - Spectral unmixing

KW - Spectroscopy

KW - Stepwise maximum angle calculation

KW - Step-wise maximum angle calculation

KW - Variable purity

U2 - 10.1016/B978-0-12-409547-2.14747-X

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BT - Comprehensive Cheometircs

A2 - Brown, Steven

A2 - Tauler, Romà

A2 - Walczak, Beata

PB - Elsevier

ER -

Two-Way Data Analysis: Detection of Purest Variables

Abstract

Keywords

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