dc.creator | Ríos-Reina, Rocío | es |
dc.creator | Azcárate, Silvana M. | es |
dc.creator | Camiña, José M. | es |
dc.creator | Goicoechea, Héctor C. | es |
dc.date.accessioned | 2023-03-03T15:41:47Z | |
dc.date.available | 2023-03-03T15:41:47Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Ríos-Reina, R., Azcárate, S.M., Camiña, J.M. y Goicoechea, H.C. (2020). Multi-level Data Fusion Strategies for Modeling Three-way Electrophoresis Capillary and Fluorescence Arrays Enhancing Geographical and Grape Variety Classification of Wines. Analytica Chimica Acta, 1126, 52-62. https://doi.org/10.1016/j.aca.2020.06.014. | |
dc.identifier.issn | 0003-2670 | es |
dc.identifier.issn | 1873-4324 | es |
dc.identifier.uri | https://hdl.handle.net/11441/143147 | |
dc.description.abstract | Capillary electrophoresis with diode array detection (CE-DAD) and multidimensional fluorescence spectroscopy (EEM) second-order data were fused and chemometrically processed for geographical and grape variety classification of wines. Multi-levels data fusion strategies on three-way data were evaluated and compared revealing their advantages/disadvantages in the classification context. Straightforward approaches based on a series of data preprocessing and feature extraction steps were developed for each studied level. Partial least square discriminant analysis (PLS-DA) and its multi-way extension (NPLS-DA) were applied to CE-DAD, EEM and fused data matrices structured as two-way and three-way arrays, respectively. Classification results achieved on each model were evaluated through global indices such as average sensitivity non-error rate and average precision. Different degrees of improvement were observed comparing the fused matrix results with those obtained using a single one, clear benefits have been demonstrated when level of data fusion increases, achieving with the high-level strategy the best classification results. | es |
dc.description.sponsorship | Consejo Nacional de Investigaciones Científicas y Técnicas PIP-2015 Nº 011 | es |
dc.description.sponsorship | Agencia Nacional de Promoción Científica y Tecnológica PICT 2017-0340, PICT-2018-04496 | es |
dc.format | application/pdf | es |
dc.format.extent | 41 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Analytica Chimica Acta, 1126, 52-62. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Classification | es |
dc.subject | Electrophoresis capillary | es |
dc.subject | Multi-level data fusion | es |
dc.subject | Multidimensional fluorescence spectroscopy | es |
dc.subject | Three-way data modeling | es |
dc.title | Multi-level Data Fusion Strategies for Modeling Three-way Electrophoresis Capillary and Fluorescence Arrays Enhancing Geographical and Grape Variety Classification of Wines | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Nutrición y Bromatología, Toxicología y Medicina Legal | es |
dc.relation.projectID | PIP-2015 Nº 0111 | es |
dc.relation.projectID | PICT 2017-0340 | es |
dc.relation.projectID | PICT-2018-04496 | es |
dc.relation.publisherversion | https://dx.doi.org/10.1016/j.aca.2020.06.014 | es |
dc.identifier.doi | 10.1016/j.aca.2020.06.014 | es |
dc.journaltitle | Analytica Chimica Acta | es |
dc.publication.volumen | 1126 | es |
dc.publication.initialPage | 52 | es |
dc.publication.endPage | 62 | es |
dc.contributor.funder | Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Argentina | es |
dc.contributor.funder | Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT). Argentina | es |