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-25333
Full metadata record
DC FieldValueLanguage
dc.contributor.authorVenturini, Francesca-
dc.contributor.authorSperti, Michela-
dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorGucciardi, Arnaud-
dc.contributor.authorMartos, Vanessa M.-
dc.contributor.authorDeriu, Marco A.-
dc.date.accessioned2022-07-27T07:36:24Z-
dc.date.available2022-07-27T07:36:24Z-
dc.date.issued2022-07-
dc.identifier.issn0260-8774de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25333-
dc.description.abstractOne of the main challenges for olive oil producers is the ability to assess oil quality regularly during the production cycle. The quality of olive oil is evaluated through a series of parameters that can be determined, up to now, only through multiple chemical analysis techniques. This requires samples to be sent to approved laboratories, making the quality control an expensive, time-consuming process, that cannot be performed regularly and cannot guarantee the quality of oil up to the point it reaches the consumer. This work presents a new approach that is fast and based on low-cost instrumentation, and which can be easily performed in the field. The proposed method is based on fluorescence spectroscopy and one-dimensional convolutional neural networks and allows to predict five chemical quality indicators of olive oil (acidity, peroxide value, UV spectroscopic parameters K270 and K232, and ethyl esters) from one single fluorescence spectrum obtained with a very fast measurement from a low-cost portable fluorescence sensor. The results indicate that the proposed approach gives exceptional results for quality determination through the extraction of the relevant physicochemical parameters. This would make the continuous quality control of olive oil during and after the entire production cycle a reality.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofJournal of Food Engineeringde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectFluorescence spectroscopyde_CH
dc.subjectOptical sensorde_CH
dc.subjectOlive oilde_CH
dc.subjectQuality controlde_CH
dc.subjectConvolutional neural networkde_CH
dc.subjectMachine learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc621.3: Elektro-, Kommunikations-, Steuerungs- und Regelungstechnikde_CH
dc.titleExtraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oilde_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.doi10.1016/j.jfoodeng.2022.111198de_CH
dc.identifier.doi10.21256/zhaw-25333-
zhaw.funding.euNode_CH
zhaw.issue111198de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume336de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedPhotonicsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
2022_Venturini-etal_Extraction-of-physicochemical-properties-olive-oil.pdf1.99 MBAdobe PDFThumbnail
View/Open
Show simple item record
Venturini, F., Sperti, M., Michelucci, U., Gucciardi, A., Martos, V. M., & Deriu, M. A. (2022). Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil. Journal of Food Engineering, 336(111198). https://doi.org/10.1016/j.jfoodeng.2022.111198
Venturini, F. et al. (2022) ‘Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil’, Journal of Food Engineering, 336(111198). Available at: https://doi.org/10.1016/j.jfoodeng.2022.111198.
F. Venturini, M. Sperti, U. Michelucci, A. Gucciardi, V. M. Martos, and M. A. Deriu, “Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil,” Journal of Food Engineering, vol. 336, no. 111198, Jul. 2022, doi: 10.1016/j.jfoodeng.2022.111198.
VENTURINI, Francesca, Michela SPERTI, Umberto MICHELUCCI, Arnaud GUCCIARDI, Vanessa M. MARTOS und Marco A. DERIU, 2022. Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks : application to olive oil. Journal of Food Engineering. Juli 2022. Bd. 336, Nr. 111198. DOI 10.1016/j.jfoodeng.2022.111198
Venturini, Francesca, Michela Sperti, Umberto Michelucci, Arnaud Gucciardi, Vanessa M. Martos, and Marco A. Deriu. 2022. “Extraction of Physicochemical Properties from the Fluorescence Spectrum with 1D Convolutional Neural Networks : Application to Olive Oil.” Journal of Food Engineering 336 (111198). https://doi.org/10.1016/j.jfoodeng.2022.111198.
Venturini, Francesca, et al. “Extraction of Physicochemical Properties from the Fluorescence Spectrum with 1D Convolutional Neural Networks : Application to Olive Oil.” Journal of Food Engineering, vol. 336, no. 111198, July 2022, https://doi.org/10.1016/j.jfoodeng.2022.111198.


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