Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-26697Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Schmid, N. | - |
| dc.contributor.author | Bruderer, S. | - |
| dc.contributor.author | Paruzzo, F. | - |
| dc.contributor.author | Fischetti, G. | - |
| dc.contributor.author | Toscano, G. | - |
| dc.contributor.author | Graf, D. | - |
| dc.contributor.author | Fey, M. | - |
| dc.contributor.author | Henrici, A. | - |
| dc.contributor.author | Ziebart, V. | - |
| dc.contributor.author | Heitmann, B. | - |
| dc.contributor.author | Grabner, H. | - |
| dc.contributor.author | Wegner, J.D. | - |
| dc.contributor.author | Sigel, R.K.O. | - |
| dc.contributor.author | Wilhelm, D. | - |
| dc.date.accessioned | 2023-01-26T08:58:35Z | - |
| dc.date.available | 2023-01-26T08:58:35Z | - |
| dc.date.issued | 2023-02 | - |
| dc.identifier.issn | 1090-7807 | de_CH |
| dc.identifier.issn | 1096-0856 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/26697 | - |
| dc.description.abstract | The analysis of nuclear magnetic resonance (NMR) spectra to detect peaks and characterize their parameters, often referred to as deconvolution, is a crucial step in the quantification, elucidation, and verification of the structure of molecular systems. However, deconvolution of 1D NMR spectra is a challenge for both experts and machines. We propose a robust, expert-level quality deep learning-based deconvolution algorithm for 1D experimental NMR spectra. The algorithm is based on a neural network trained on synthetic spectra. Our customized pre-processing and labeling of the synthetic spectra enable the estimation of critical peak parameters. Furthermore, the neural network model transfers well to the experimental spectra and demonstrates low fitting errors and sparse peak lists in challenging scenarios such as crowded, high dynamic range, shoulder peak regions as well as broad peaks. We demonstrate in challenging spectra that the proposed algorithm is superior to expert results. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Elsevier | de_CH |
| dc.relation.ispartof | Journal of Magnetic Resonance | de_CH |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | de_CH |
| dc.subject | NMR spectroscopy | de_CH |
| dc.subject | Deconvolution | de_CH |
| dc.subject | Machine learning | de_CH |
| dc.subject | Deep learning | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.subject.ddc | 530: Physik | de_CH |
| dc.title | Deconvolution of 1D NMR spectra : a deep learning-based approach | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Angewandte Mathematik und Physik (IAMP) | de_CH |
| zhaw.organisationalunit | Institut für Datenanalyse und Prozessdesign (IDP) | de_CH |
| dc.identifier.doi | 10.1016/j.jmr.2022.107357 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-26697 | - |
| dc.identifier.pmid | 36563418 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.issue | 107357 | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.volume | 347 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.webfeed | Datalab | de_CH |
| zhaw.funding.zhaw | Maschinelles Lernen für NMR-Spektroskopie | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_Schmid-etal_Deconvolution-of-1D-NMR-spectra_JMR.pdf | 2.43 MB | Adobe PDF |  View/Open |
Show simple item record
Schmid, N., Bruderer, S., Paruzzo, F., Fischetti, G., Toscano, G., Graf, D., Fey, M., Henrici, A., Ziebart, V., Heitmann, B., Grabner, H., Wegner, J. D., Sigel, R. K. O., & Wilhelm, D. (2023). Deconvolution of 1D NMR spectra : a deep learning-based approach. Journal of Magnetic Resonance, 347(107357). https://doi.org/10.1016/j.jmr.2022.107357
Schmid, N. et al. (2023) ‘Deconvolution of 1D NMR spectra : a deep learning-based approach’, Journal of Magnetic Resonance, 347(107357). Available at: https://doi.org/10.1016/j.jmr.2022.107357.
N. Schmid et al., “Deconvolution of 1D NMR spectra : a deep learning-based approach,” Journal of Magnetic Resonance, vol. 347, no. 107357, Feb. 2023, doi: 10.1016/j.jmr.2022.107357.
SCHMID, N., S. BRUDERER, F. PARUZZO, G. FISCHETTI, G. TOSCANO, D. GRAF, M. FEY, A. HENRICI, V. ZIEBART, B. HEITMANN, H. GRABNER, J.D. WEGNER, R.K.O. SIGEL und D. WILHELM, 2023. Deconvolution of 1D NMR spectra : a deep learning-based approach. Journal of Magnetic Resonance. Februar 2023. Bd. 347, Nr. 107357. DOI 10.1016/j.jmr.2022.107357
Schmid, N., S. Bruderer, F. Paruzzo, G. Fischetti, G. Toscano, D. Graf, M. Fey, et al. 2023. “Deconvolution of 1D NMR Spectra : A Deep Learning-Based Approach.” Journal of Magnetic Resonance 347 (107357). https://doi.org/10.1016/j.jmr.2022.107357.
Schmid, N., et al. “Deconvolution of 1D NMR Spectra : A Deep Learning-Based Approach.” Journal of Magnetic Resonance, vol. 347, no. 107357, Feb. 2023, https://doi.org/10.1016/j.jmr.2022.107357.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.