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Multi‐block methods in multivariate process control
Author(s) -
Kohonen Jarno,
Reinikainen SatuPia,
Aaljoki Kari,
Perkiö Annikki,
Väänänen Taito,
Höskuldsson Agnar
Publication year - 2008
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/cem.1199
Subject(s) - data matrix , process (computing) , matrix (chemical analysis) , block (permutation group theory) , multivariate statistics , computer science , data mining , process control , chemometrics , mathematics , machine learning , chemistry , chromatography , clade , biochemistry , geometry , operating system , phylogenetic tree , gene
In chemometric studies all predictor variables are usually collected in one data matrix X . This matrix is then analyzed by PLS regression or other methods. When data from several different sub‐processes are collected in one matrix, there is a possibility that the effects of some sub‐processes may vanish. If there is, for instance, mechanic data from one process and spectral data from another, the influence of the mechanic sub‐process may not be detected. An application of multi‐block (MB) methods, where the X ‐data are divided into several data blocks is presented in this study. By using MB methods the effect of a sub‐process can be seen and an example with two blocks, near infra‐red, NIR, and process data, is shown. The results show improvements in modelling task, when a MB‐based approach is used. This way of working with data gives more information on the process than if all data are in one X ‐matrix. The procedure is demonstrated by an industrial continuous process, where knowledge about the sub‐processes is available and X ‐matrix can be divided into blocks between process variables and NIR spectra. Copyright © 2008 John Wiley & Sons, Ltd.