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dc.contributor.author | Vitale, Raffaele![]() |
es_ES |
dc.contributor.author | Noord, Onno E. de![]() |
es_ES |
dc.contributor.author | Westerhuis, Johan A.![]() |
es_ES |
dc.contributor.author | Smilde, Age K.![]() |
es_ES |
dc.contributor.author | Ferrer, Alberto![]() |
es_ES |
dc.date.accessioned | 2022-09-20T18:04:07Z | |
dc.date.available | 2022-09-20T18:04:07Z | |
dc.date.issued | 2021-02 | es_ES |
dc.identifier.issn | 0886-9383 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/186410 | |
dc.description.abstract | [EN] The possibility of addressing the problem of process troubleshooting and understanding by modelling common and distinctive sources of variation (factorsorcomponents) underlying two sets of measurements was explored in a real-world industrial case study. The used strategy includes a novel approach to systematically detect the number of common and distinctive components. An extension of this strategy for the analysis of a larger number of data blocks, which allows the comparison of data from multiple processing units, is also discussed. | es_ES |
dc.description.sponsorship | Spanish Ministry of Economy and Competitiveness, Grant/Award Number: DPI2017-82896-C2-1-R | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | John Wiley & Sons | es_ES |
dc.relation.ispartof | Journal of Chemometrics | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Canonical correlation analysis (CCA) | es_ES |
dc.subject | Common components | es_ES |
dc.subject | Distinctive components | es_ES |
dc.subject | Permutation testing | es_ES |
dc.subject | Singular value decomposition (SVD) | es_ES |
dc.subject.classification | ESTADISTICA E INVESTIGACION OPERATIVA | es_ES |
dc.title | Divide et impera: How disentangling common and distinctive variability in multiset data analysis can aid industrial process troubleshooting and understanding | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1002/cem.3266 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-82896-C2-1-R/ES/DISEÑO, CARACTERIZACION Y AJUSTE OPTIMO DE BIOCIRCUITOS SINTETICOS PARA BIOPRODUCCION CON CONTROL DE CARGA METABOLICA/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat | es_ES |
dc.description.bibliographicCitation | Vitale, R.; Noord, OED.; Westerhuis, JA.; Smilde, AK.; Ferrer, A. (2021). Divide et impera: How disentangling common and distinctive variability in multiset data analysis can aid industrial process troubleshooting and understanding. Journal of Chemometrics. 35(2):1-12. https://doi.org/10.1002/cem.3266 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1002/cem.3266 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 12 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 35 | es_ES |
dc.description.issue | 2 | es_ES |
dc.relation.pasarela | S\448295 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |