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https://doi.org/10.21256/zhaw-30952| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| 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 10.21256/zhaw-30952 |
| Proceedings: | Data Science for Photonics and Biophotonics |
| Editors of the parent work: | Bocklitz, Thomas |
| Page(s): | 130110A |
| Conference details: | SPIE Photonics Europe, Strasbourg, France, 7-11 April 2024 |
| Issue Date: | 18-Jun-2024 |
| Series: | Proceedings of SPIE |
| Series volume: | 13011 |
| Publisher / Ed. Institution: | SPIE |
| ISBN: | 9781510673403 9781510673410 |
| Language: | English |
| Subjects: | Fluorescence spectroscopy; UV spectroscopy; Olive oil; Food quality control; Machine learning |
| 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. Two different machine learning pipelines were implemented for the prediction of K232 and K268. In a first approach, the model was trained on all the ten ageing steps of each oil and learned to predict all the ten steps of an unseen oil. In a second approach, the model was trained on one single ageing on multiple oils and step for all the oils and learned to predicta single ageing step. The results demonstrate the potential of the proposed approach. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/30952 |
| 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) |
| Published as part of the ZHAW project: | ARES - AI for fluoREscence Spectroscopy in oil |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2024_Gucciardi-etal_ML-feature-extraction-predicting-olive-oil-ageing_SPIE.pdf | 1.01 MB | Adobe PDF |  View/Open |
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Gucciardi, A., El Ghazouali, S., Michelucci, U., & Venturini, F. (2024). Machine learning feature extraction for predicting the ageing of olive oil [Conference paper]. In T. Bocklitz (Ed.), Data Science for Photonics and Biophotonics (p. 130110A). SPIE. https://doi.org/10.1117/12.3017680
Gucciardi, A. et al. (2024) ‘Machine learning feature extraction for predicting the ageing of olive oil’, in T. Bocklitz (ed.) Data Science for Photonics and Biophotonics. SPIE, p. 130110A. 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 Data Science for Photonics and Biophotonics, Jun. 2024, p. 130110A. 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: Thomas BOCKLITZ (Hrsg.), Data Science for Photonics and Biophotonics. Conference paper. SPIE. 18 Juni 2024. S. 130110A. ISBN 9781510673403
Gucciardi, Arnaud, Safouane El Ghazouali, Umberto Michelucci, and Francesca Venturini. 2024. “Machine Learning Feature Extraction for Predicting the Ageing of Olive Oil.” Conference paper. In Data Science for Photonics and Biophotonics, edited by Thomas Bocklitz, 130110A. SPIE. https://doi.org/10.1117/12.3017680.
Gucciardi, Arnaud, et al. “Machine Learning Feature Extraction for Predicting the Ageing of Olive Oil.” Data Science for Photonics and Biophotonics, edited by Thomas Bocklitz, SPIE, 2024, p. 130110A, https://doi.org/10.1117/12.3017680.
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