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dc.contributor.authorSperti, Michela-
dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorVenturini, Francesca-
dc.contributor.authorGucciardi, Arnaud-
dc.contributor.authorDeriu, Marco A.-
dc.date.accessioned2022-04-22T15:17:20Z-
dc.date.available2022-04-22T15:17:20Z-
dc.date.issued2022-04-06-
dc.identifier.urihttps://spie.org/EPE/conferencedetails/optical-sensing-detectionde_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/24836-
dc.descriptionOptical Sensing and Detection VII: 12139-81de_CH
dc.description.abstractThe chemical analysis of food is essential to monitor and guarantee its quality. The determination of the chemical parameters, like the concentration of particular substances, is performed by specialized laboratories and is a time-consuming and costly process. Therefore, alternative methods with easier handling are of great interest. Among these fluorescence spectroscopy offers great opportunities. Fluorescence spectra are one-dimensional arrays of values already successfully employed together with artificial neural networks for classification problems in chemistry, physics, and other fields. However, the extraction of specific quantities from the spectra poses a much harder challenge. This work analyzes and compares the ability of feed-forward neural networks (FFNN) and one-dimensional convolutional neural networks (1D-CNN) to extract relevant features from fluorescence spectra of olive oils. The results indicate that 1D-CNN, contrary to FFNN, successfully predicts the chemical parameters with high accuracy. The great advantages of the proposed method are: 1) the possibility of using optical methods instead of time-consuming chemical ones, like chromatography, 2) the lack of any special sample handling, like dilution and 3) the lack of any pre-processing of the data. The problem of small datasets, which may arise for novel techniques like the proposed one, is also addressed statistically by using the leave-one-out resampling technique.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectFluorescence spectroscopyde_CH
dc.subjectOptical sensorde_CH
dc.subjectOlive oilde_CH
dc.subjectArtificial neural networkde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc540: Chemiede_CH
dc.titleChemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networksde_CH
dc.typeKonferenz: Posterde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
zhaw.conference.detailsSPIE Photonics Europe, Strasbourg, France, 3-7 April 2022de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.webfeedPhotonicsde_CH
zhaw.funding.zhawSelf-learning optical sensorde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Sperti, M., Michelucci, U., Venturini, F., Gucciardi, A., & Deriu, M. A. (2022, April 6). Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks. SPIE Photonics Europe, Strasbourg, France, 3-7 April 2022. https://spie.org/EPE/conferencedetails/optical-sensing-detection
Sperti, M. et al. (2022) ‘Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks’, in SPIE Photonics Europe, Strasbourg, France, 3-7 April 2022. Available at: https://spie.org/EPE/conferencedetails/optical-sensing-detection.
M. Sperti, U. Michelucci, F. Venturini, A. Gucciardi, and M. A. Deriu, “Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks,” in SPIE Photonics Europe, Strasbourg, France, 3-7 April 2022, Apr. 2022. [Online]. Available: https://spie.org/EPE/conferencedetails/optical-sensing-detection
SPERTI, Michela, Umberto MICHELUCCI, Francesca VENTURINI, Arnaud GUCCIARDI und Marco A. DERIU, 2022. Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks. In: SPIE Photonics Europe, Strasbourg, France, 3-7 April 2022 [online]. Conference poster. 6 April 2022. Verfügbar unter: https://spie.org/EPE/conferencedetails/optical-sensing-detection
Sperti, Michela, Umberto Michelucci, Francesca Venturini, Arnaud Gucciardi, and Marco A. Deriu. 2022. “Chemical Analysis of Olive Oils from Fluorescence Spectra Thanks to One-Dimensional Convolutional Neural Networks.” Conference poster. In SPIE Photonics Europe, Strasbourg, France, 3-7 April 2022. https://spie.org/EPE/conferencedetails/optical-sensing-detection.
Sperti, Michela, et al. “Chemical Analysis of Olive Oils from Fluorescence Spectra Thanks to One-Dimensional Convolutional Neural Networks.” SPIE Photonics Europe, Strasbourg, France, 3-7 April 2022, 2022, https://spie.org/EPE/conferencedetails/optical-sensing-detection.


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