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dc.contributor.authorRamírez-Morales, Iván
dc.contributor.authorRivero, Daniel
dc.contributor.authorFernández-Blanco, Enrique
dc.contributor.authorPazos, A.
dc.date.accessioned2017-04-24T10:38:06Z
dc.date.issued2016-10-14
dc.identifier.citationRamírez-Morales I, Rivero D, Fernández-Blanco E, Pazos A. Optimization of NIR calibration models for multiple processes in the sugar industry. Chemometr Intell Lab Syst. 2016; 159:45-57es_ES
dc.identifier.issn0169-7439
dc.identifier.urihttp://hdl.handle.net/2183/18419
dc.description.abstract[Abstract] The measurements of Near-Infrared (NIR) Spectroscopy, combined with data analysis techniques, are widely used for quality control in food production processes. This paper presents a methodology to optimize the calibration models of NIR spectra in four different stages in a sugar factory. The models were designed for quality monitoring, particularly °Brix and Sucrose, both common parameters in the sugar industry. A three stage optimization methodology, including pre-processing selection, feature selection and support vector machines regression metaparameters tuning, were applied to the spectral data divided by repeated cross-validation. Global models were optimized while endeavoring to ensure they are able to estimate both quality parameters with a single calibration, for the four steps of the process. The proposed models improve the prediction for the test set (unseen data) compared to previously published models, resulting in a more accurate quality assessment of the intermediate products of the process in the sugar industry.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relation.urihttp://doi.org/10.1016/j.chemolab.2016.10.003es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectNIRes_ES
dc.subjectChemometricses_ES
dc.subjectCalibration modelses_ES
dc.subjectMachine learninges_ES
dc.subjectSupport vector machineses_ES
dc.subjectAgro-industryes_ES
dc.titleOptimization of NIR calibration models for multiple processes in the sugar industryes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/embargoedAccesses_ES
dc.date.embargoEndDate2018-10-14es_ES
dc.date.embargoLift2018-10-14
UDC.journalTitleChemometrics and Intelligent Laboratory Systemses_ES
UDC.volume159es_ES
UDC.startPage45es_ES
UDC.endPage57es_ES


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