Two-Way Data Analysis: Detection of Purest Variables

Willem Windig, Andrey Bogomolov*, Sergey Kucheryavskiy

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Publikation: Bidrag til bog/antologi/rapport/konference proceedingBidrag til bog/antologiForskningpeer review

Abstrakt

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.
OriginalsprogEngelsk
TitelReference Module in Chemistry, Molecular Sciences and Chemical Engineering
Antal sider33
ForlagElsevier
Publikationsdato2020
Sider275-307
ISBN (Elektronisk)9780124095472
DOI
StatusUdgivet - 2020

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Citationsformater

Windig, W., Bogomolov, A., & Kucheryavskiy, S. (2020). Two-Way Data Analysis: Detection of Purest Variables. I Reference Module in Chemistry, Molecular Sciences and Chemical Engineering (s. 275-307). Elsevier. https://doi.org/10.1016/B978-0-12-409547-2.14747-X