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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30952
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dc.contributor.authorGucciardi, Arnaud-
dc.contributor.authorEl Ghazouali, Safouane-
dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorVenturini, Francesca-
dc.date.accessioned2024-06-28T10:15:18Z-
dc.date.available2024-06-28T10:15:18Z-
dc.date.issued2024-06-18-
dc.identifier.isbn9781510673403de_CH
dc.identifier.isbn9781510673410de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30952-
dc.description.abstractMonitoring the quality of extra virgin olive oil (EVOO) during its life cycle is of particular importance due to its influence on health-related characteristics and its significance for the oil industry. For this reason it is critical to find an easy-to-perform, non-destructive and affordable method to monitor the quality of EVOO and detect its degradation due to ageing. The following study explores a machine learning approach based on fluorescence measurements for predicting oil changes arising from the ageing process. The proposed method specifically predicts the quality parameters that are required for an olive oil to qualify as extra virgin. In particular, the two properties considered in this analysis are the UV absorbance at 232 and 268 nm (K232 and K268), both critical markers of the quality of extra virgin oil. To achieve this goal, a large dataset of fluorescence measurements was analysed, comprising 720 excitation-emission matrices of twenty-four different oils initially labeled as extra virgin. The samples were aged under accelerated conditions at 60 ◦C in the dark for nine weeks and their properties were measured at ten different time steps during the process. Two different machine learning pipelines were implemented for the prediction of K232 and K268. In a first approach, the model was trained on all the ten ageing steps of each oil and learned to predict all the ten steps of an unseen oil. In a second approach, the model was trained on one single ageing on multiple oils and step for all the oils and learned to predicta single ageing step. The results demonstrate the potential of the proposed approach.de_CH
dc.language.isoende_CH
dc.publisherSPIEde_CH
dc.relation.ispartofseriesProceedings of SPIEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectFluorescence spectroscopyde_CH
dc.subjectUV spectroscopyde_CH
dc.subjectOlive oilde_CH
dc.subjectFood quality controlde_CH
dc.subjectMachine learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc540: Chemiede_CH
dc.titleMachine learning feature extraction for predicting the ageing of olive oilde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
dc.identifier.doi10.1117/12.3017680de_CH
dc.identifier.doi10.21256/zhaw-30952-
zhaw.conference.detailsSPIE Photonics Europe, Strasbourg, France, 7-11 April 2024de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start130110Ade_CH
zhaw.parentwork.editorBocklitz, Thomas-
zhaw.publication.statuspublishedVersionde_CH
zhaw.series.number13011de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsData Science for Photonics and Biophotonicsde_CH
zhaw.webfeedPhotonicsde_CH
zhaw.funding.zhawARES - AI for fluoREscence Spectroscopy in oilde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Gucciardi, A., El Ghazouali, S., Michelucci, U., & Venturini, F. (2024). Machine learning feature extraction for predicting the ageing of olive oil [Conference paper]. In T. Bocklitz (Ed.), Data Science for Photonics and Biophotonics (p. 130110A). SPIE. https://doi.org/10.1117/12.3017680
Gucciardi, A. et al. (2024) ‘Machine learning feature extraction for predicting the ageing of olive oil’, in T. Bocklitz (ed.) Data Science for Photonics and Biophotonics. SPIE, p. 130110A. Available at: https://doi.org/10.1117/12.3017680.
A. Gucciardi, S. El Ghazouali, U. Michelucci, and F. Venturini, “Machine learning feature extraction for predicting the ageing of olive oil,” in Data Science for Photonics and Biophotonics, Jun. 2024, p. 130110A. doi: 10.1117/12.3017680.
GUCCIARDI, Arnaud, Safouane EL GHAZOUALI, Umberto MICHELUCCI und Francesca VENTURINI, 2024. Machine learning feature extraction for predicting the ageing of olive oil. In: Thomas BOCKLITZ (Hrsg.), Data Science for Photonics and Biophotonics. Conference paper. SPIE. 18 Juni 2024. S. 130110A. ISBN 9781510673403
Gucciardi, Arnaud, Safouane El Ghazouali, Umberto Michelucci, and Francesca Venturini. 2024. “Machine Learning Feature Extraction for Predicting the Ageing of Olive Oil.” Conference paper. In Data Science for Photonics and Biophotonics, edited by Thomas Bocklitz, 130110A. SPIE. https://doi.org/10.1117/12.3017680.
Gucciardi, Arnaud, et al. “Machine Learning Feature Extraction for Predicting the Ageing of Olive Oil.” Data Science for Photonics and Biophotonics, edited by Thomas Bocklitz, SPIE, 2024, p. 130110A, https://doi.org/10.1117/12.3017680.


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