Shedding light on the ageing of extra virgin olive oil : probing the impact of temperature with fluorescence spectroscopy and machine learning techniques

Venturini, Francesca; Fluri, Silvan; Mejari, Manas; Baumgartner, Michael; Piga, Dario; Michelucci, Umberto

doi:10.1016/j.lwt.2023.115679

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29630

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Shedding light on the ageing of extra virgin olive oil : probing the impact of temperature with fluorescence spectroscopy and machine learning techniques
Authors:	Venturini, Francesca Fluri, Silvan Mejari, Manas Baumgartner, Michael Piga, Dario Michelucci, Umberto
et. al:	No
DOI:	10.1016/j.lwt.2023.115679 10.21256/zhaw-29630
Published in:	LWT - Food Science and Technology
Volume(Issue):	191
Issue:	115679
Issue Date:	Jan-2024
Publisher / Ed. Institution:	Elsevier
ISSN:	0023-6438
Language:	English
Subjects:	Machine learning; Oxidation; Fluorescence spectroscopy; Absorption spectroscopy; Olive oil
Subject (DDC):	006: Special computer methods 540: Chemistry
Abstract:	This work systematically investigates the oxidation of extra virgin olive oil (EVOO) under accelerated storage conditions with UV absorption and total fluorescence spectroscopy. With the large amount of data collected, it proposes a method to monitor the oil’s quality based on machine learning (ML) applied to highly-aggregated data. EVOO is a high-quality vegetable oil that has earned worldwide reputation for its numerous health benefits and excellent taste. Despite its outstanding quality, EVOO degrades over time due to oxidation, which can affect both its health qualities and flavour. Therefore, it is highly relevant to quantify the effects of oxidation on EVOO and develop methods to assess it that can be easily implemented under field conditions, rather than in specialized analytical laboratories. The ML approach indicates that the two excitation wavelengths (480 nm) and (300 nm) exhibit the maximum relative change in fluorescence intensity during the ageing for the majority of the oils, thus identifying the wavelengths which are more informative for quality prediction. Also, the paper proposes a method for the prediction of olive oil quality using highly-aggregated data. Such a method is of interest because it paves the way to the realization of a low-cost, portable device for in-field quality control. The following study demonstrates that fluorescence spectroscopy has the capability to monitor the effect of oxidation and assess the quality of EVOO, even when the data are highly aggregated. It shows that complex laboratory equipment is not necessary to exploit fluorescence spectroscopy using the proposed method and that cost-effective solutions, which can be used in-field by non-scientists, could provide an easily-accessible assessment of the quality of EVOO.
URI:	https://digitalcollection.zhaw.ch/handle/11475/29630
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
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

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Venturini, F., Fluri, S., Mejari, M., Baumgartner, M., Piga, D., & Michelucci, U. (2024). Shedding light on the ageing of extra virgin olive oil : probing the impact of temperature with fluorescence spectroscopy and machine learning techniques. LWT - Food Science and Technology, 191(115679). https://doi.org/10.1016/j.lwt.2023.115679

Venturini, F. et al. (2024) ‘Shedding light on the ageing of extra virgin olive oil : probing the impact of temperature with fluorescence spectroscopy and machine learning techniques’, LWT - Food Science and Technology, 191(115679). Available at: https://doi.org/10.1016/j.lwt.2023.115679.

F. Venturini, S. Fluri, M. Mejari, M. Baumgartner, D. Piga, and U. Michelucci, “Shedding light on the ageing of extra virgin olive oil : probing the impact of temperature with fluorescence spectroscopy and machine learning techniques,” LWT - Food Science and Technology, vol. 191, no. 115679, Jan. 2024, doi: 10.1016/j.lwt.2023.115679.

VENTURINI, Francesca, Silvan FLURI, Manas MEJARI, Michael BAUMGARTNER, Dario PIGA und Umberto MICHELUCCI, 2024. Shedding light on the ageing of extra virgin olive oil : probing the impact of temperature with fluorescence spectroscopy and machine learning techniques. LWT - Food Science and Technology. Januar 2024. Bd. 191, Nr. 115679. DOI 10.1016/j.lwt.2023.115679

Venturini, Francesca, Silvan Fluri, Manas Mejari, Michael Baumgartner, Dario Piga, and Umberto Michelucci. 2024. “Shedding Light on the Ageing of Extra Virgin Olive Oil : Probing the Impact of Temperature with Fluorescence Spectroscopy and Machine Learning Techniques.” LWT - Food Science and Technology 191 (115679). https://doi.org/10.1016/j.lwt.2023.115679.

Venturini, Francesca, et al. “Shedding Light on the Ageing of Extra Virgin Olive Oil : Probing the Impact of Temperature with Fluorescence Spectroscopy and Machine Learning Techniques.” LWT - Food Science and Technology, vol. 191, no. 115679, Jan. 2024, https://doi.org/10.1016/j.lwt.2023.115679.