Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil

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

doi:10.1117/12.2621588

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25329

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil
Authors:	Arnaud, Gucciardi Michelucci, Umberto Venturini, Francesca Sperti, Michela Martos, Vanessa M. Deriu, Marco A.
et. al:	No
DOI:	10.1117/12.2621588 10.21256/zhaw-25329
Proceedings:	Optical Sensing and Detection VII
Editors of the parent work:	Berghmans, Francis Zergioti, Ioanna
Page(s):	121391J
Conference details:	SPIE Photonics Europe, Strasbourg, France, 3-7 April 2022
Issue Date:	May-2022
Series:	Proceedings of SPIE
Series volume:	12139
Publisher / Ed. Institution:	Society of Photo-Optical Instrumentation Engineers (SPIE)
ISBN:	9781510651548 9781510651555
ISSN:	0277-786X 1996-756X
Language:	English
Subjects:	Fluorescence spectroscopy; Fluorescence sensor; Olive oil; Machine learning; Convolutional neural network; Quality control
Subject (DDC):	006: Special computer methods 540: Chemistry
Abstract:	Olive oil is an important commodity in the world, and its demand has grown substantially in recent years. As of today, the determination of olive oil quality is based on both chemical analysis and organoleptic evaluation from specialized laboratories and panels of experts, thus resulting in a complex and time-consuming process. This work presents a new compact and low-cost sensor based on fluorescence spectroscopy and artificial neural networks that can perform olive oil quality assessment. The presented sensor has the advantage of being a portable, easy-to-use, and low-cost device, which works with undiluted samples, and without any pre-processing of data, thus simplifying the analysis to the maximum degree possible. Different artificial neural networks were analyzed and their performance compared. To deal with the heterogeneity in the samples, as producer or harvest year, a novel neural network architecture is presented, called here conditional convolutional neural network (Cond- CNN). The presented technology is demonstrated by analyzing olive oils of different quality levels and from different producers: extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO). The sensor classifies the oils in the three mentioned classes with an accuracy of 82%. These results indicate that the Cond-CNN applied to the data obtained with the low-cost luminescence sensor, can deal with a set of oils coming from multiple producers, and, therefore, showing quite heterogeneous chemical characteristics.
URI:	https://digitalcollection.zhaw.ch/handle/11475/25329
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

Files in This Item:

File	Description	Size	Format
2022_Gucciardi-etal_Compact-optical-fluorescence_SPIE_121391J.pdf		2.83 MB	Adobe PDF	View/Open

Show full item record

Arnaud, G., Michelucci, U., Venturini, F., Sperti, M., Martos, V. M., & Deriu, M. A. (2022). Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil [Conference paper]. In F. Berghmans & I. Zergioti (Eds.), Optical Sensing and Detection VII (p. 121391J). Society of Photo-Optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.2621588

Arnaud, G. et al. (2022) ‘Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil’, in F. Berghmans and I. Zergioti (eds) Optical Sensing and Detection VII. Society of Photo-Optical Instrumentation Engineers (SPIE), p. 121391J. Available at: https://doi.org/10.1117/12.2621588.

G. Arnaud, U. Michelucci, F. Venturini, M. Sperti, V. M. Martos, and M. A. Deriu, “Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil,” in Optical Sensing and Detection VII, May 2022, p. 121391J. doi: 10.1117/12.2621588.

ARNAUD, Gucciardi, Umberto MICHELUCCI, Francesca VENTURINI, Michela SPERTI, Vanessa M. MARTOS und Marco A. DERIU, 2022. Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil. In: Francis BERGHMANS und Ioanna ZERGIOTI (Hrsg.), Optical Sensing and Detection VII. Conference paper. Society of Photo-Optical Instrumentation Engineers (SPIE). Mai 2022. S. 121391J. ISBN 9781510651548

Arnaud, Gucciardi, Umberto Michelucci, Francesca Venturini, Michela Sperti, Vanessa M. Martos, and Marco A. Deriu. 2022. “Compact Optical Fluorescence Sensor for Food Quality Control Using Artificial Neural Networks: Application to Olive Oil.” Conference paper. In Optical Sensing and Detection VII, edited by Francis Berghmans and Ioanna Zergioti, 121391J. Society of Photo-Optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.2621588.

Arnaud, Gucciardi, et al. “Compact Optical Fluorescence Sensor for Food Quality Control Using Artificial Neural Networks: Application to Olive Oil.” Optical Sensing and Detection VII, edited by Francis Berghmans and Ioanna Zergioti, Society of Photo-Optical Instrumentation Engineers (SPIE), 2022, p. 121391J, https://doi.org/10.1117/12.2621588.