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Optimization of NIR Calibration Models for Multiple Processes in the Sugar Industry
dc.contributor.author | Ramírez-Morales, Iván | |
dc.contributor.author | Rivero, Daniel | |
dc.contributor.author | Fernández-Blanco, Enrique | |
dc.contributor.author | Pazos, A. | |
dc.date.accessioned | 2017-04-24T10:38:06Z | |
dc.date.issued | 2016-10-14 | |
dc.identifier.citation | Ramírez-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; 159:45-57 | es_ES |
dc.identifier.issn | 0169-7439 | |
dc.identifier.uri | http://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.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.uri | http://doi.org/10.1016/j.chemolab.2016.10.003 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | NIR | es_ES |
dc.subject | Chemometrics | es_ES |
dc.subject | Calibration models | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Support vector machines | es_ES |
dc.subject | Agro-industry | es_ES |
dc.title | Optimization of NIR Calibration Models for Multiple Processes in the Sugar Industry | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/embargoedAccess | es_ES |
dc.date.embargoEndDate | 2018-10-14 | es_ES |
dc.date.embargoLift | 2018-10-14 | |
UDC.journalTitle | Chemometrics and Intelligent Laboratory Systems | es_ES |
UDC.volume | 159 | es_ES |
UDC.startPage | 45 | es_ES |
UDC.endPage | 57 | es_ES |
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