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

RÍOS-REINA, R.; AZCARATE, SILVANA M.; CAMIÑA, JOSÉ M.; GOICOECHEA, HÉCTOR C.

Paraiba

Workshop; 3. XI Workshop de Quimiometria; 2020

Over the years, analytical methods and data analysis tools commonly used in food quality and process control had to be re-evaluated and modified to fit these new tasks. In this progression of gathering more and better information, the multivariate statistical analysis of fused data has become a powerful tool for enhancing the reliability of the results1. Being the key point how the information sources can be combined to provide the joint classification prediction of the samples, three levels of data fusion (DF) have been reported2. The aim of this work was to develop multiple strategies to assess the three DF levels on two second- order arrays, with different data complexity, in order to know the correlation and analogy between both information sources for twofold classification purposes. Thus, the challenge consisted in finding the optimal combination of data preprocessing, fused data and data modeling that would provide the best results.