A framework for sequential multiblock component methods

Authors

  • Age K. Smilde,

    Corresponding author
    1. Process Analysis and Chemometrics, Department of Chemical Engineering, University of Amsterdam, Nieuwe Achtergracht 166, NL-1018 WV Amsterdam, The Netherlands
    2. TNO Nutrition and Food Research, Utrechtseweg 48, NL-3700 AJ Zeist, The Netherlands
    • Process Analysis and Chemometrics, Department of Chemical Engineering, University of Amsterdam, Nieuwe Achtergracht 166, NL-1018 WV Amsterdam, The Netherlands.
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  • Johan A. Westerhuis,

    1. Process Analysis and Chemometrics, Department of Chemical Engineering, University of Amsterdam, Nieuwe Achtergracht 166, NL-1018 WV Amsterdam, The Netherlands
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  • Sijmen de Jong

    1. Unilever Research and Development Vlaardingen, PO Box 114, NL-3130 AC Vlaardingen, The Netherlands
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Abstract

Multiblock or multiset methods are starting to be used in chemistry and biology to study complex data sets. In chemometrics, sequential multiblock methods are popular; that is, methods that calculate one component at a time and use deflation for finding the next component. In this paper a framework is provided for sequential multiblock methods, including hierarchical PCA (HPCA; two versions), consensus PCA (CPCA; two versions) and generalized PCA (GPCA). Properties of the methods are derived and characteristics of the methods are discussed. All this is illustrated with a real five-block example from chromatography. The only methods with clear optimization criteria are GPCA and one version of CPCA. Of these, GPCA is shown to give inferior results compared with CPCA. Copyright © 2003 John Wiley & Sons, Ltd.

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