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18 June 2024 Machine learning feature extraction for predicting the ageing of olive oil
Arnaud Gucciardi, Safouane El Ghazouali, Umberto Michelucci, Francesca Venturini
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Proceedings Volume 13011, Data Science for Photonics and Biophotonics; 130110A (2024) https://doi.org/10.1117/12.3017680
Event: SPIE Photonics Europe, 2024, Strasbourg, France
Abstract
Monitoring the quality of extra virgin olive oil (EVOO) during its life cycle is of particular importance due to its influence on health-related characteristics and its significance for the oil industry. For this reason it is critical to find an easy-to-perform, non-destructive and affordable method to monitor the quality of EVOO and detect its degradation due to ageing. The following study explores a machine learning approach based on fluorescence measurements for predicting oil changes arising from the ageing process. The proposed method specifically predicts the quality parameters that are required for an olive oil to qualify as extra virgin. In particular, the two properties considered in this analysis are the UV absorbance at 232 and 268 nm (K232 and K268), both critical markers of the quality of extra virgin oil. To achieve this goal, a large dataset of fluorescence measurements was analysed, comprising 720 excitation-emission matrices of twenty-four different oils initially labeled as extra virgin. The samples were aged under accelerated conditions at 60 °C in the dark for nine weeks and their properties were measured at ten different time steps during the process.
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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Arnaud Gucciardi, Safouane El Ghazouali, Umberto Michelucci, and Francesca Venturini "Machine learning feature extraction for predicting the ageing of olive oil", Proc. SPIE 13011, Data Science for Photonics and Biophotonics, 130110A (18 June 2024); https://doi.org/10.1117/12.3017680
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KEYWORDS
Machine learning
Absorbance
Fluorescence spectroscopy
Feature extraction
Performance modeling
Food inspection
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