Please use this identifier to cite or link to this item:
|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Exploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques|
Caballero, Josep Palau
Deriu, Marco Agostino
|Publisher / Ed. Institution:||MDPI|
|Subjects:||Fluorescence spectroscopy; Fluorescence sensor; Quality control; Olive oil; Machine learning; Artificial neural networks|
|Subject (DDC):||006: Special computer methods |
|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 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.|
|Further description:||This article belongs to the Special Issue Advanced Analysis Methods for Food Safety, Authenticity and Traceability Assessment|
|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:||Self-learning optical sensor|
|Appears in collections:||Publikationen School of Engineering|
Files in This Item:
|2021_Venturini-etal_Exploration-of-Spanish-olive-oil-quality.pdf||1.57 MB||Adobe PDF|
Show full item record
Venturini, F., Sperti, M., Michelucci, U., Herzig, I., Baumgartner, M., Caballero, J. P., Jimenez, A., & Deriu, M. A. (2021). Exploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques. Foods, 10(5), 1010. https://doi.org/10.3390/foods10051010
Venturini, F. et al. (2021) ‘Exploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques’, Foods, 10(5), p. 1010. Available at: https://doi.org/10.3390/foods10051010.
F. Venturini et al., “Exploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques,” Foods, vol. 10, no. 5, p. 1010, May 2021, doi: 10.3390/foods10051010.
Venturini, Francesca, et al. “Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques.” Foods, vol. 10, no. 5, May 2021, p. 1010, https://doi.org/10.3390/foods10051010.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.