Multiway data modeling for enhancing classification performance: fluorescence data as case of study.

AZCARATE, SILVANA MARIELA; ZALDARRIAGA HEREDIA, JORGELINA; MIRTA R. ALCARAZ; CAMIÑA, JOSÉ MANUEL; GOICOECHEA, HÉCTOR C.

Congreso; XVIII Chemometrics in Analytical Chemistry; 2022

In the framework of multivariate classification, there is a continuous need for improving methods for identification and characterization. For quantitativepurposes, the increase in the order of the data has provided certain benefits concerning the performance of the analytical method as the improvement ofselectivity and sensitivity. However, the advantages gained by increasing the order of the data to solve a classification problem have not been deeply studiedyet. This work aims to explore the data acquisition, feature extraction, and analysis methods of multi-way data arrays to improve the performance of themethod to classify olive oils according to extraction process as proof of concept.