Data augmentation in food science: synthesising spectroscopic data of vegetable oils for performance enhancement

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Abstract

Generating more accurate, efficient, and robust classification models in chemometrics, able to address real‐world problems in food analysis, is intrinsically related with the amount of available calibration samples. In this paper, we propose a data augmentation solution to increase the performance of a classification model by generating realistic data augmented samples. The feasibility of this solution has been evaluated on 3 main different experiments where Fourier transform mid infrared (FT‐IR) spectroscopic data of vegetable oils were used for the identification of vegetable oil species in oil admixtures. Results demonstrate that data augmented samples improved the classification rate by around 19% in a single instrument validation and provided a significant 38% improvement in classification when testing in more than 10 different spectroscopic instruments to the calibration one.
Original languageEnglish
Article numbere3004
Number of pages15
JournalJournal of Chemometrics
Volume32
Issue number6
Early online date09 Feb 2018
DOIs
Publication statusPublished - 19 Jun 2018

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