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dc.contributor.authorRuiz Miguel, Santiago 
dc.contributor.authorSarabia Peinador, Luis Antonio 
dc.contributor.authorOrtiz Fernández, Mª Cruz 
dc.contributor.authorSánchez Pastor, Mª Sagrario 
dc.date.accessioned2020-06-23T13:42:14Z
dc.date.available2020-06-23T13:42:14Z
dc.date.issued2020-08
dc.identifier.issn0169-7439
dc.identifier.urihttp://hdl.handle.net/10259/5351
dc.description.abstractThe paper contains a discussion about the null spaces associated to linear prediction models for the particular case of Partial Least Squares regression models. The discussion separately considers the two existing null spaces: the one in the input space related to the projection onto the latent space and the null space, coming from the projection space, corresponding to the mapping of the scores onto the predicted responses. The paper also explores the impact of such null spaces in the definition of the design space around some feasible solutions obtained by inverting the prediction model, via several cases with simulated and real data from the literature. The case-studies serve to illustrate the discussion and the need of considering points in the two null spaces, rather than just take into account the null space within the latent space. They also serve to show how to address the use of the resulting vectors in the design space to maintain the desired quality by modifying the tunable (maneuverable) process variables to compensate for variations due to some other feature variables not so easy to control.en
dc.description.sponsorshipEuropean Regional Development Fund (FEDER) through Spanish Agencia Estatal de Investigación (project CTQ2017-88894-R)es
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevieres
dc.relation.ispartofChemometrics and Intelligent Laboratory Systems. 2020, V. 203, 104040es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectPartial least squaresen
dc.subjectProcess analytical technologyen
dc.subjectQuality by designen
dc.subjectLinear applicationen
dc.subjectNull spaceen
dc.subjectModel inversionen
dc.subject.otherQuímica analíticaes
dc.subject.otherChemistry, Analyticen
dc.titleResidual spaces in latent variables model inversion and their impact in the design space for given quality characteristicsen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.relation.publisherversionhttps://doi.org/10.1016/j.chemolab.2020.104040
dc.identifier.doi10.1016/j.chemolab.2020.104040
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/CTQ2017‐88894‐R
dc.journal.titleChemometrics and Intelligent Laboratory Systemses
dc.volume.number203es
dc.page.initial104040es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion


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