1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22465
Full metadata record
DC FieldValueLanguage
dc.contributor.authorVenturini, Francesca-
dc.contributor.authorSperti, Michela-
dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorHerzig, Ivo-
dc.contributor.authorBaumgartner, Michael-
dc.contributor.authorCaballero, Josep Palau-
dc.contributor.authorJimenez, Arturo-
dc.contributor.authorDeriu, Marco Agostino-
dc.date.accessioned2021-05-12T10:26:53Z-
dc.date.available2021-05-12T10:26:53Z-
dc.date.issued2021-05-06-
dc.identifier.issn2304-8158de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22465-
dc.descriptionThis article belongs to the Special Issue Advanced Analysis Methods for Food Safety, Authenticity and Traceability Assessmentde_CH
dc.description.abstractExtra 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.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofFoodsde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectFluorescence spectroscopyde_CH
dc.subjectFluorescence sensorde_CH
dc.subjectQuality controlde_CH
dc.subjectOlive oilde_CH
dc.subjectMachine learningde_CH
dc.subjectArtificial neural networksde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc540: Chemiede_CH
dc.titleExploration of Spanish olive oil quality with a miniaturized low-cost fluorescence sensor and machine learning techniquesde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
dc.identifier.doi10.3390/foods10051010de_CH
dc.identifier.doi10.21256/zhaw-22465-
zhaw.funding.euNode_CH
zhaw.issue5de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start1010de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume10de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedPhotonicsde_CH
zhaw.funding.zhawSelf-learning optical sensorde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2021_Venturini-etal_Exploration-of-Spanish-olive-oil-quality.pdf1.57 MBAdobe PDFThumbnail
View/Open
Show simple 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, 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.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.