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dc.contributor.authorValencia García, Olga 
dc.contributor.authorOrtiz Fernández, Mª Cruz 
dc.contributor.authorSarabia Peinador, Luis Antonio 
dc.date.accessioned2024-01-10T11:19:22Z
dc.date.available2024-01-10T11:19:22Z
dc.date.issued2021
dc.identifier.issn0886-9383
dc.identifier.urihttp://hdl.handle.net/10259/8281
dc.description.abstractRelative errors are typically used in chemometrics to evaluate the performance of a multivariate predictive model. However, these models are not obtained through the criterion of minimizing relative errors, as would be expected in a model whose response is the concentration of an analyte. There are no studies in chemometrics on the use of a principal component regression that minimizes the sum of the squares of the relative errors. This work proposes a model, which serves this purpose. The suggested model, wPCR, has been applied to 7 datasets with 12 predicted responses, 10 of which are multivariate calibrations of analytes in complex mixtures based on instrumental signals coming from various analytical techniques. As PCR and wPCR are methods seeking to optimize different criteria, each one achieves a better performance with respect to its own criterion. Therefore, the new model wPCR leads to better results insofar as the relative errors are considered, especially for the smallest responses. In this sense, the wPCR model also outperforms PCR with logarithmic transformation of the response (logPCR). In addition, as for the performance of the method using Joint Confidence Regions for the intercept and the slope of the accuracy line, it is shown that the application of wPCR does not introduce bias, neither constant nor proportional for the models built, nor a systematic alteration of the achievable accuracy.es
dc.description.sponsorshipThe authors thank the financial support provided by Spanish MINECO (AEI/FEDER, UE) through project CTQ2017-88894-R and Consejería de la Junta de Castilla y León (BU052P20), both co-financed with European Regional Development Fund, Junta de Castilla y León and Fondo Social Europeo.es
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherWileyes
dc.relation.ispartofJournal of Chemometrics. 2021, V. 35, n. 6, e3341es
dc.subjectAccuracy linees
dc.subjectJoint confidence regiones
dc.subjectMultivariate calibrationes
dc.subjectPrincipal component regressiones
dc.subjectRelative errores
dc.subject.otherMatemáticases
dc.subject.otherMathematicses
dc.subject.otherQuímicaes
dc.subject.otherChemistryes
dc.subject.otherQuímica analíticaes
dc.subject.otherChemistry, Analytices
dc.titlePrincipal component regression that minimizes the sum of the squares of the relative errors: Application in multivariate calibration modelses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1002/cem.3341es
dc.identifier.doi10.1002/cem.3341
dc.identifier.essn1099-128X
dc.journal.titleJournal of Chemometricses
dc.volume.number35es
dc.issue.number6es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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