Machine learning feature extraction for predicting the ageing of olive oil

Gucciardi, Arnaud; El Ghazouali, Safouane; Michelucci, Umberto; Venturini, Francesca

doi:10.1117/12.3017680

Publication type:	Conference other
Type of review:	Peer review (abstract)
Title:	Machine learning feature extraction for predicting the ageing of olive oil
Authors:	Gucciardi, Arnaud El Ghazouali, Safouane Michelucci, Umberto Venturini, Francesca
et. al:	No
DOI:	10.1117/12.3017680
Conference details:	SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024
Issue Date:	11-Apr-2024
Language:	English
Subjects:	Fluorescence spectroscopy; Machine learning (ML); Food quality control; Olive oil
Subject (DDC):	006: Special computer methods 540: Chemistry
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.
Further description:	Data Science for Photonics and Biophotonics; Paper 13011-14
URI:	https://digitalcollection.zhaw.ch/handle/11475/30604
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	School of Engineering
Organisational Unit:	Institute of Applied Mathematics and Physics (IAMP)
Appears in collections:	Publikationen School of Engineering

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Gucciardi, A., El Ghazouali, S., Michelucci, U., & Venturini, F. (2024, April 11). Machine learning feature extraction for predicting the ageing of olive oil. SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024. https://doi.org/10.1117/12.3017680

Gucciardi, A. et al. (2024) ‘Machine learning feature extraction for predicting the ageing of olive oil’, in SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024. Available at: https://doi.org/10.1117/12.3017680.

A. Gucciardi, S. El Ghazouali, U. Michelucci, and F. Venturini, “Machine learning feature extraction for predicting the ageing of olive oil,” in SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024, Apr. 2024. doi: 10.1117/12.3017680.

GUCCIARDI, Arnaud, Safouane EL GHAZOUALI, Umberto MICHELUCCI und Francesca VENTURINI, 2024. Machine learning feature extraction for predicting the ageing of olive oil. In: SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024. Conference presentation. 11 April 2024

Gucciardi, Arnaud, Safouane El Ghazouali, Umberto Michelucci, and Francesca Venturini. 2024. “Machine Learning Feature Extraction for Predicting the Ageing of Olive Oil.” Conference presentation. In SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024. https://doi.org/10.1117/12.3017680.

Gucciardi, Arnaud, et al. “Machine Learning Feature Extraction for Predicting the Ageing of Olive Oil.” SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024, 2024, https://doi.org/10.1117/12.3017680.