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https://doi.org/10.21256/zhaw-26698| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Automatic classification of signal regions in 1H nuclear magnetic resonance spectra |
| Autor/-in: | Fischetti, Giulia Schmid, Nicolas Bruderer, Simon Caldarelli, Guido Scarso, Alessandro Henrici, Andreas Wilhelm, Dirk |
| et. al: | No |
| DOI: | 10.3389/frai.2022.1116416 10.21256/zhaw-26698 |
| Erschienen in: | Frontiers in Artificial Intelligence |
| Band(Heft): | 5 |
| Heft: | 1116416 |
| Erscheinungsdatum: | 11-Jan-2023 |
| Verlag / Hrsg. Institution: | Frontiers Research Foundation |
| ISSN: | 2624-8212 |
| Sprache: | Englisch |
| Schlagwörter: | Nuclear magnetic resonance; Automatic signal classification; Deep learning; 1H spectra; Multiplet assignment |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 530: Physik |
| Zusammenfassung: | The 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/26698 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Angewandte Mathematik und Physik (IAMP) |
| Publiziert im Rahmen des ZHAW-Projekts: | Maschinelles Lernen für NMR-Spektroskopie |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2023_Fischetti-etal_Automatic-classification-signal-regions-1H-NMR-spectra.pdf | 1.22 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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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