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Comparing alternative approaches for multivariate statistical analysis of batch process data
Author(s) -
Westerhuis Johan A.,
Kourti Theodora,
MacGregor John F.
Publication year - 1999
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/(sici)1099-128x(199905/08)13:3/4<397::aid-cem559>3.0.co;2-i
Subject(s) - batch processing , principal component analysis , computer science , context (archaeology) , data matrix , process (computing) , multivariate statistics , data mining , variable (mathematics) , matrix (chemical analysis) , component analysis , mathematics , artificial intelligence , machine learning , chemistry , chromatography , operating system , paleontology , clade , biochemistry , mathematical analysis , gene , biology , programming language , phylogenetic tree
Batch process data can be arranged in a three‐way matrix (batch × variable × time). This paper provides a critical discussion of various aspects of the treatment of these multiway data. First, several methods that have been proposed for decomposing three‐way data matrices are discussed in the context of batch process data analysis and monitoring. These methods are multiway principal component analysis (MPCA)—also called Tucker1—parallel factor analysis (PARAFAC) and Tucker3. Secondly, different ways of unfolding, mean centering and scaling the three‐way matrix are compared and discussed with respect to their effects on the analysis of batch data. Finally, the role of the time variable in batch process data is considered and methods suggested to predict the per cent completion of batch runs with unequal duration are discussed. Copyright © 1999 John Wiley & Sons, Ltd.