Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil

Venturini, Francesca; Sperti, Michela; Michelucci, Umberto; Gucciardi, Arnaud; Martos, Vanessa M.; Deriu, Marco A.

doi:10.1016/j.jfoodeng.2022.111198

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25333

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil
Authors:	Venturini, Francesca Sperti, Michela Michelucci, Umberto Gucciardi, Arnaud Martos, Vanessa M. Deriu, Marco A.
et. al:	No
DOI:	10.1016/j.jfoodeng.2022.111198 10.21256/zhaw-25333
Published in:	Journal of Food Engineering
Volume(Issue):	336
Issue:	111198
Issue Date:	Jul-2022
Publisher / Ed. Institution:	Elsevier
ISSN:	0260-8774
Language:	English
Subjects:	Fluorescence spectroscopy; Optical sensor; Olive oil; Quality control; Convolutional neural network; Machine learning
Subject (DDC):	006: Special computer methods 621.3: Electrical, communications, control engineering
Abstract:	One of the main challenges for olive oil producers is the ability to assess oil quality regularly during the production cycle. The quality of olive oil is evaluated through a series of parameters that can be determined, up to now, only through multiple chemical analysis techniques. This requires samples to be sent to approved laboratories, making the quality control an expensive, time-consuming process, that cannot be performed regularly and cannot guarantee the quality of oil up to the point it reaches the consumer. This work presents a new approach that is fast and based on low-cost instrumentation, and which can be easily performed in the field. The proposed method is based on fluorescence spectroscopy and one-dimensional convolutional neural networks and allows to predict five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters K270 and K232, and ethyl esters) from one single fluorescence spectrum obtained with a very fast measurement from a low-cost portable fluorescence sensor. The results indicate that the proposed approach gives exceptional results for quality determination through the extraction of the relevant physicochemical parameters. This would make the continuous quality control of olive oil during and after the entire production cycle a reality.
URI:	https://digitalcollection.zhaw.ch/handle/11475/25333
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)
Appears in collections:	Publikationen School of Engineering

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Venturini, F., Sperti, M., Michelucci, U., Gucciardi, A., Martos, V. M., & Deriu, M. A. (2022). Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil. Journal of Food Engineering, 336(111198). https://doi.org/10.1016/j.jfoodeng.2022.111198

Venturini, F. et al. (2022) ‘Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil’, Journal of Food Engineering, 336(111198). Available at: https://doi.org/10.1016/j.jfoodeng.2022.111198.

F. Venturini, M. Sperti, U. Michelucci, A. Gucciardi, V. M. Martos, and M. A. Deriu, “Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil,” Journal of Food Engineering, vol. 336, no. 111198, Jul. 2022, doi: 10.1016/j.jfoodeng.2022.111198.

VENTURINI, Francesca, Michela SPERTI, Umberto MICHELUCCI, Arnaud GUCCIARDI, Vanessa M. MARTOS und Marco A. DERIU, 2022. Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil. Journal of Food Engineering. Juli 2022. Bd. 336, Nr. 111198. DOI 10.1016/j.jfoodeng.2022.111198

Venturini, Francesca, Michela Sperti, Umberto Michelucci, Arnaud Gucciardi, Vanessa M. Martos, and Marco A. Deriu. 2022. “Extraction of Physicochemical Properties from the Fluorescence Spectrum with 1D Convolutional Neural Networks : Application to Olive Oil.” Journal of Food Engineering 336 (111198). https://doi.org/10.1016/j.jfoodeng.2022.111198.

Venturini, Francesca, et al. “Extraction of Physicochemical Properties from the Fluorescence Spectrum with 1D Convolutional Neural Networks : Application to Olive Oil.” Journal of Food Engineering, vol. 336, no. 111198, July 2022, https://doi.org/10.1016/j.jfoodeng.2022.111198.