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  2. School of Engineering
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  4. Publikationen School of Engineering
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dc.contributor.authorVenturini, Francesca-
dc.contributor.authorMichelucci, Umberto-
dc.contributor.authorSperti, Michela-
dc.contributor.authorGucciardi, Arnaud-
dc.contributor.authorDeriu, Marco A.-
dc.date.accessioned2023-02-17T09:18:09Z-
dc.date.available2023-02-17T09:18:09Z-
dc.date.issued2023-02-01-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/27038-
dc.descriptionAI and Optical Data Sciences IV; Paper 12438-50de_CH
dc.description.abstractThe power of artificial neural networks to determine the quality and properties of olive oil was proven by several studies in the last years. Less clear is, however, how the neural network is able to extract useful information from the input data. This work investigates the learning mechanism of one-dimensional convolutional neural networks (1D-CNNs) trained to predict the physicochemical properties of olive oil from single fluorescence spectra. Such a 1D-CNN can successfully predict the parameters relevant to the quality assessment: acidity, peroxide value, and UV absorbance. To go beyond a simple quality assessment algorithm, it is important to identify which spectral features in the measured spectra are correlated with each chemical parameter and therefore with the quality of olive oil. To obtain this information, explainability techniques can be used by studying the latent feature space generated by the intermediate layers of the one-dimensional trained convolutional neural network. This work analyses in detail the common features that are used by the 1D-CNN to predict the two physicochemical parameters: acidity and K232.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectFluorescencede_CH
dc.subjectArtificial intelligencede_CH
dc.subjectMachine learningde_CH
dc.subjectConvolutional neural networkde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc530: Physikde_CH
dc.titleUnderstanding the learning mechanism of convolutional neural networks applied to fluorescence spectrade_CH
dc.typeKonferenz: Sonstigesde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Angewandte Mathematik und Physik (IAMP)de_CH
zhaw.conference.detailsSPIE Photonics West, San Francisco, USA, 28 January - 2 February 2023de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.webfeedPhotonicsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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Venturini, F., Michelucci, U., Sperti, M., Gucciardi, A., & Deriu, M. A. (2023, February 1). Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra. SPIE Photonics West, San Francisco, USA, 28 January - 2 February 2023.
Venturini, F. et al. (2023) ‘Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra’, in SPIE Photonics West, San Francisco, USA, 28 January - 2 February 2023.
F. Venturini, U. Michelucci, M. Sperti, A. Gucciardi, and M. A. Deriu, “Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra,” in SPIE Photonics West, San Francisco, USA, 28 January - 2 February 2023, Feb. 2023.
VENTURINI, Francesca, Umberto MICHELUCCI, Michela SPERTI, Arnaud GUCCIARDI und Marco A. DERIU, 2023. Understanding the learning mechanism of convolutional neural networks applied to fluorescence spectra. In: SPIE Photonics West, San Francisco, USA, 28 January - 2 February 2023. Conference presentation. 1 Februar 2023
Venturini, Francesca, Umberto Michelucci, Michela Sperti, Arnaud Gucciardi, and Marco A. Deriu. 2023. “Understanding the Learning Mechanism of Convolutional Neural Networks Applied to Fluorescence Spectra.” Conference presentation. In SPIE Photonics West, San Francisco, USA, 28 January - 2 February 2023.
Venturini, Francesca, et al. “Understanding the Learning Mechanism of Convolutional Neural Networks Applied to Fluorescence Spectra.” SPIE Photonics West, San Francisco, USA, 28 January - 2 February 2023, 2023.


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