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Article

Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques

1
Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Technikumstrasse 9, 8401 Winterthur, Switzerland
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TOELT LLC, Birchlenstr. 25, 8600 Dübendorf, Switzerland
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Polito BIO Med Lab., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
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School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
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SCA San Sebastián Puente del Ventorro, s/n, 18566 Benalua de las Villas, Spain
*
Authors to whom correspondence should be addressed.
Academic Editor: Angela Di Pinto
Foods 2021, 10(5), 1010; https://doi.org/10.3390/foods10051010
Received: 9 April 2021 / Revised: 29 April 2021 / Accepted: 30 April 2021 / Published: 6 May 2021
Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires advanced equipment and chemical knowledge of certified laboratories, and has therefore limited accessibility. In this work a minimalist, portable, and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing the classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential to substitute expensive and complex chemical analysis. View Full-Text
Keywords: fluorescence spectroscopy; fluorescence sensor; olive oil; machine learning; artificial neural networks; quality control fluorescence spectroscopy; fluorescence sensor; olive oil; machine learning; artificial neural networks; quality control
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MDPI and ACS Style

Venturini, F.; Sperti, M.; Michelucci, U.; Herzig, I.; Baumgartner, M.; Caballero, J.P.; Jimenez, A.; Deriu, M.A. Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques. Foods 2021, 10, 1010. https://doi.org/10.3390/foods10051010

AMA Style

Venturini F, Sperti M, Michelucci U, Herzig I, Baumgartner M, Caballero JP, Jimenez A, Deriu MA. Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques. Foods. 2021; 10(5):1010. https://doi.org/10.3390/foods10051010

Chicago/Turabian Style

Venturini, Francesca, Michela Sperti, Umberto Michelucci, Ivo Herzig, Michael Baumgartner, Josep P. Caballero, Arturo Jimenez, and Marco A. Deriu. 2021. "Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques" Foods 10, no. 5: 1010. https://doi.org/10.3390/foods10051010

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