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dc.contributor.authorErgon, Rolf
dc.date.accessioned2007-02-08T09:20:06Z
dc.date.accessioned2017-04-19T12:49:49Z
dc.date.available2007-02-08T09:20:06Z
dc.date.available2017-04-19T12:49:49Z
dc.date.issued2004
dc.identifier.citationJournal of process control 14 (2004), No. 8, p. 889-897
dc.identifier.issn0959-1524
dc.identifier.urihttp://hdl.handle.net/11250/2438411
dc.description.abstractPrincipal component regression (PCR) based on principal component analysis (PCA) and partial least squares regression (PLSR) are well known projection methods for analysis of multivariate data. They result in scores and loadings that may be visualized in a score-loading plot (biplot) and used for process monitoring. The difficulty with this is that often more than two principal or PLS components have to be used, resulting in a need to monitor more than one such plot. However, it has recently been shown that for a scalar response variable all PLSR/PCR models can be compressed into equivalent PLSR models with two components only. After a summary of the underlying theory, the present paper shows how such two-component PLS (2PLS) models can be utilized in informative score-loading biplots for process understanding and monitoring. The possible utilization of known projection model monitoring statistics and variable contribution plots is also discussed, and a new method for visualization of contributions directly in the biplot is presented. An industrial data example is included.
dc.format.extent357734 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.subjectPLS
dc.subjectScore-loading correspondence
dc.subjectBiplot
dc.subjectProcess understanding and monitoring
dc.titleInformative PLS score-loading plots for process understanding
dc.typeJournal article
dc.typePeer reviewed
dc.subject.nsi563
dc.identifier.doihttp://dx.doi.org/10.1016/j.jprocont.2004.02.004


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