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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26698
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dc.contributor.authorFischetti, Giulia-
dc.contributor.authorSchmid, Nicolas-
dc.contributor.authorBruderer, Simon-
dc.contributor.authorCaldarelli, Guido-
dc.contributor.authorScarso, Alessandro-
dc.contributor.authorHenrici, Andreas-
dc.contributor.authorWilhelm, Dirk-
dc.date.accessioned2023-01-26T08:59:41Z-
dc.date.available2023-01-26T08:59:41Z-
dc.date.issued2023-01-11-
dc.identifier.issn2624-8212de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/26698-
dc.description.abstractThe identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in 1H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental 1H NMR spectra.de_CH
dc.language.isoende_CH
dc.publisherFrontiers Research Foundationde_CH
dc.relation.ispartofFrontiers in Artificial Intelligencede_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectNuclear magnetic resonancede_CH
dc.subjectAutomatic signal classificationde_CH
dc.subjectDeep learningde_CH
dc.subject1H spectrade_CH
dc.subjectMultiplet assignmentde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc530: Physikde_CH
dc.titleAutomatic classification of signal regions in 1H nuclear magnetic resonance spectrade_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.doi10.3389/frai.2022.1116416de_CH
dc.identifier.doi10.21256/zhaw-26698-
zhaw.funding.euNode_CH
zhaw.issue1116416de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume5de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedDatalabde_CH
zhaw.funding.zhawMaschinelles Lernen für NMR-Spektroskopiede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Fischetti, G., Schmid, N., Bruderer, S., Caldarelli, G., Scarso, A., Henrici, A., & Wilhelm, D. (2023). Automatic classification of signal regions in 1H nuclear magnetic resonance spectra. Frontiers in Artificial Intelligence, 5(1116416). https://doi.org/10.3389/frai.2022.1116416
Fischetti, G. et al. (2023) ‘Automatic classification of signal regions in 1H nuclear magnetic resonance spectra’, Frontiers in Artificial Intelligence, 5(1116416). Available at: https://doi.org/10.3389/frai.2022.1116416.
G. Fischetti et al., “Automatic classification of signal regions in 1H nuclear magnetic resonance spectra,” Frontiers in Artificial Intelligence, vol. 5, no. 1116416, Jan. 2023, doi: 10.3389/frai.2022.1116416.
FISCHETTI, Giulia, Nicolas SCHMID, Simon BRUDERER, Guido CALDARELLI, Alessandro SCARSO, Andreas HENRICI und Dirk WILHELM, 2023. Automatic classification of signal regions in 1H nuclear magnetic resonance spectra. Frontiers in Artificial Intelligence. 11 Januar 2023. Bd. 5, Nr. 1116416. DOI 10.3389/frai.2022.1116416
Fischetti, Giulia, Nicolas Schmid, Simon Bruderer, Guido Caldarelli, Alessandro Scarso, Andreas Henrici, and Dirk Wilhelm. 2023. “Automatic Classification of Signal Regions in 1H Nuclear Magnetic Resonance Spectra.” Frontiers in Artificial Intelligence 5 (1116416). https://doi.org/10.3389/frai.2022.1116416.
Fischetti, Giulia, et al. “Automatic Classification of Signal Regions in 1H Nuclear Magnetic Resonance Spectra.” Frontiers in Artificial Intelligence, vol. 5, no. 1116416, Jan. 2023, https://doi.org/10.3389/frai.2022.1116416.


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