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A chemometrics toolbox based on projections and latent variables
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC))
Umeå University, Faculty of Science and Technology, Department of Chemistry.
2014 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, no 5, 332-346 p.Article in journal (Refereed) Published
Abstract [en]

A personal view is given about the gradual development of projection methods-also called bilinear, latent variable, and more-and their use in chemometrics. We start with the principal components analysis (PCA) being the basis for more elaborate methods for more complex problems such as soft independent modeling of class analogy, partial least squares (PLS), hierarchical PCA and PLS, PLS-discriminant analysis, Orthogonal projection to latent structures (OPLS), OPLS-discriminant analysis and more. From its start around 1970, this development was strongly influenced by Bruce Kowalski and his group in Seattle, and his realization that the multidimensional data profiles emerging from spectrometers, chromatographs, and other electronic instruments, contained interesting information that was not recognized by the current one variable at a time approaches to chemical data analysis. This led to the adoption of what in statistics is called the data analytical approach, often called also the data driven approach, soft modeling, and more. This approach combined with PCA and later PLS, turned out to work very well in the analysis of chemical data. This because of the close correspondence between, on the one hand, the matrix decomposition at the heart of PCA and PLS and, on the other hand, the analogy concept on which so much of chemical theory and experimentation are based. This extends to numerical and conceptual stability and good approximation properties of these models. The development is informally summarized and described and illustrated by a few examples and anecdotes.

Place, publisher, year, edition, pages
John Wiley & Sons, 2014. Vol. 28, no 5, 332-346 p.
Keyword [en]
Chemometrics, Latent variables, OPLS, PLS, Projection methods
National Category
Chemical Sciences
URN: urn:nbn:se:umu:diva-86943DOI: 10.1002/cem.2581ISI: 000335520900003OAI: diva2:704972
Available from: 2014-03-13 Created: 2014-03-13 Last updated: 2014-07-08Bibliographically approved

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Trygg, JohanWold, Svante
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