Ríos-Reina, RocíoAzcárate, Silvana M.Camiña, José M.Goicoechea, Héctor C.2023-03-032023-03-032020Ríos-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.2020.06.014.0003-26701873-4324https://hdl.handle.net/11441/143147Capillary 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.application/pdf41 p.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/ClassificationElectrophoresis capillaryMulti-level data fusionMultidimensional fluorescence spectroscopyThree-way data modelinginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1016/j.aca.2020.06.014