Deconvolution of 1D NMR spectra : a deep learning-based approach

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.

doi:10.1016/j.jmr.2022.107357

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26697

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Deconvolution of 1D NMR spectra : a deep learning-based approach
Authors:	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.
et. al:	No
DOI:	10.1016/j.jmr.2022.107357 10.21256/zhaw-26697
Published in:	Journal of Magnetic Resonance
Volume(Issue):	347
Issue:	107357
Issue Date:	Feb-2023
Publisher / Ed. Institution:	Elsevier
ISSN:	1090-7807 1096-0856
Language:	English
Subjects:	NMR spectroscopy; Deconvolution; Machine learning; Deep learning
Subject (DDC):	006: Special computer methods 530: Physics
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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/26697
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Institute of Applied Mathematics and Physics (IAMP) Institute of Data Analysis and Process Design (IDP)
Published as part of the ZHAW project:	Maschinelles Lernen für NMR-Spektroskopie
Appears in collections:	Publikationen School of Engineering

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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.