Divide et impera : How disentangling common and distinctive variability in multiset data analysis can aid industrial process troubleshooting and understanding-Reference-Cited by-同舟云学术

Divide et impera : How disentangling common and distinctive variability in multiset data analysis can aid industrial process troubleshooting and understanding

Published:2020-06-28 Issue:2 Volume:35 Page:
ISSN:0886-9383
Container-title:Journal of Chemometrics
language:en
Short-container-title:Journal of Chemometrics

Author:

Vitale Raffaele¹²³^ORCID,Noord Onno E.⁴,Westerhuis Johan A.⁵^ORCID,Smilde Age K.⁵^ORCID,Ferrer Alberto¹

Affiliation:

1. Grupo de Ingeniería Estadística Multivariante, Departamento de Estadística e Investigación Operativa Aplicadas y Calidad Universitat Politècnica de València Camino de Vera s/n Valencia 46022 Spain

2. Molecular Imaging and Photonics Unit, Department of Chemistry Katholieke Universiteit Leuven Celestijnenlaan 200F Leuven B‐3001 Belgium

3. U. Lille, CNRS, LASIRE, Laboratoire de Spectroscopie pour les Interactions, la Réactivité et l’Environnement, Cité scientifique Lille F‐59000 France

4. Advanced Data Analysis Consultancy Kinderhuissingel 112 Haarlem 2013 AW The Netherlands

5. Biosystems Data Analysis, Swammerdam Institute for Life Sciences University of Amsterdam Science Park 904 Amsterdam 1098 XH The Netherlands

Publisher

Wiley

Subject

Applied Mathematics,Analytical Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cem.3266

Reference29 articles.

1. Bilinear modelling of batch processes. Part I: theoretical discussion

2. Bilinear modelling of batch processes. Part II: a comparison of PLS soft-sensors

3. Bilinear modeling of batch processes. Part III: parameter stability

4. Multivariate SPC Methods for Process and Product Monitoring

5. Multivariate SPC Charts for Monitoring Batch Processes

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