Multi-level Data Fusion Strategies for Modeling Three-way Electrophoresis Capillary and Fluorescence Arrays Enhancing Geographical and Grape Variety Classification of Wines

Description

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.

Abstract

Consejo Nacional de Investigaciones Científicas y Técnicas PIP-2015 Nº 011

Abstract

Agencia Nacional de Promoción Científica y Tecnológica PICT 2017-0340, PICT-2018-04496

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