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https://doi.org/10.21256/zhaw-22465| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Exploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques |
| Autor/-in: | Venturini, Francesca Sperti, Michela Michelucci, Umberto Herzig, Ivo Baumgartner, Michael Caballero, Josep Palau Jimenez, Arturo Deriu, Marco Agostino |
| et. al: | No |
| DOI: | 10.3390/foods10051010 10.21256/zhaw-22465 |
| Erschienen in: | Foods |
| Band(Heft): | 10 |
| Heft: | 5 |
| Seite(n): | 1010 |
| Erscheinungsdatum: | 6-Mai-2021 |
| Verlag / Hrsg. Institution: | MDPI |
| ISSN: | 2304-8158 |
| Sprache: | Englisch |
| Schlagwörter: | Fluorescence spectroscopy; Fluorescence sensor; Quality control; Olive oil; Machine learning; Artificial neural networks |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 540: Chemie |
| Zusammenfassung: | 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. |
| Weitere Angaben: | This article belongs to the Special Issue Advanced Analysis Methods for Food Safety, Authenticity and Traceability Assessment |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/22465 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Angewandte Mathematik und Physik (IAMP) |
| Publiziert im Rahmen des ZHAW-Projekts: | Self-learning optical sensor |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2021_Venturini-etal_Exploration-of-Spanish-olive-oil-quality.pdf | 1.57 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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, Michela SPERTI, Umberto MICHELUCCI, Ivo HERZIG, Michael BAUMGARTNER, Josep Palau CABALLERO, Arturo JIMENEZ und Marco Agostino DERIU, 2021. Exploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniques. Foods. 6 Mai 2021. Bd. 10, Nr. 5, S. 1010. DOI 10.3390/foods10051010
Venturini, Francesca, Michela Sperti, Umberto Michelucci, Ivo Herzig, Michael Baumgartner, Josep Palau Caballero, Arturo Jimenez, and Marco Agostino Deriu. 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, 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.
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