Version 1
: Received: 5 April 2021 / Approved: 7 April 2021 / Online: 7 April 2021 (15:18:52 CEST)
How to cite:
Venturini, F.; Sperti, M.; Michelucci, U.; Herzig, I.; Baumgartner, M.; Palau Caballero, J.; Jimenez, A.; Deriu, M.A. Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques. Preprints2021, 2021040206
Venturini, F.; Sperti, M.; Michelucci, U.; Herzig, I.; Baumgartner, M.; Palau Caballero, J.; Jimenez, A.; Deriu, M.A. Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques. Preprints 2021, 2021040206
Venturini, F.; Sperti, M.; Michelucci, U.; Herzig, I.; Baumgartner, M.; Palau Caballero, J.; Jimenez, A.; Deriu, M.A. Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques. Preprints2021, 2021040206
APA Style
Venturini, F., Sperti, M., Michelucci, U., Herzig, I., Baumgartner, M., Palau Caballero, J., Jimenez, A., & Deriu, M.A. (2021). Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques. Preprints. https://doi.org/
Chicago/Turabian Style
Venturini, F., Arturo Jimenez and Marco Agostino Deriu. 2021 "Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques" Preprints. https://doi.org/
Abstract
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 the advanced equipment and chemical knowledge of certified laboratories, and has therefore a 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 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 of substituting expensive and complex chemical analysis.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
