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Data augmentation in food science: Synthesising spectroscopic data of vegetable oils for performance enhancement
Author(s) -
Georgouli Konstantia,
Osorio Maria Teresa,
Martinez Del Rincon Jesus,
Koidis Anastasios
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3004
Subject(s) - chemometrics , calibration , identification (biology) , computer science , vegetable oil , edible oil , process engineering , artificial intelligence , pattern recognition (psychology) , mathematics , chemistry , machine learning , statistics , food science , engineering , botany , biology
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.