Deconvolution of NMR spectra : a deep learning-based approach

Schmid, Nicolas; Bruderer, Simon; Fischetti, Giulia; Paruzzo, Federico; Toscano, Giuseppe; Graf, Dominik; Fey, Michael; Henrici, Andreas; Grabner, Helmut; Wegner, Jan Dirk; Sigel, Roland K. O.; Heitmann, Björn; Wilhelm, Dirk

doi:10.21256/zhaw-27336

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-27336

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DC Field	Value	Language
dc.contributor.author	Schmid, Nicolas	-
dc.contributor.author	Bruderer, Simon	-
dc.contributor.author	Fischetti, Giulia	-
dc.contributor.author	Paruzzo, Federico	-
dc.contributor.author	Toscano, Giuseppe	-
dc.contributor.author	Graf, Dominik	-
dc.contributor.author	Fey, Michael	-
dc.contributor.author	Henrici, Andreas	-
dc.contributor.author	Grabner, Helmut	-
dc.contributor.author	Wegner, Jan Dirk	-
dc.contributor.author	Sigel, Roland K. O.	-
dc.contributor.author	Heitmann, Björn	-
dc.contributor.author	Wilhelm, Dirk	-
dc.date.accessioned	2023-03-14T10:40:31Z	-
dc.date.available	2023-03-14T10:40:31Z	-
dc.date.issued	2022-07-13	-
dc.identifier.uri	https://euromar2022.org/wp-content/uploads/2022/07/Euromar-AbstractBook_2022-A4_22jul_new.pdf	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/27336	-
dc.description.abstract	We introduce a deep learning-based deconvolution approach for 1H NMR spectra, developed by leveraging concepts from the field of physics informed-learning, intelligent labeling, and tailored high dynamic range (HDR) spectral preprocessing. Since automation and faster workflows are major concerns in NMR spectroscopy, the algorithm handles uncorrected spectra without strict assumptions on phase and baseline correction as well as line shape. Due to the lack of high quality and consistently labeled experimental spectra in quantities needed to train modern deep learning models, we relied on synthetic spectra creation. Moreover, instead of training with synthetic spectra consisting of single lines, we created synthetic multiples that further supported a realistic deconvolution. We achieved super-human performance on corrected and uncorrected synthetic spectra. Finally, and most importantly, the results on synthetic data translate well to experimental spectra despite the covariate shift. Thus, this tool is a promising candidate for automated expert-level deconvolution of experimental HDR 1H NMR spectra.	de_CH
dc.language.iso	en	de_CH
dc.publisher	ZHAW Zürcher Hochschule für Angewandte Wissenschaften	de_CH
dc.rights	Licence according to publishing contract	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 NMR spectra : a deep learning-based approach	de_CH
dc.type	Konferenz: Poster	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	10.21256/zhaw-27336	-
zhaw.conference.details	European Conference on Magnetic Resonance (EUROMAR), Utrecht, The Netherlands, 10-14 July 2022	de_CH
zhaw.funding.eu	No	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.pages.start	242	de_CH
zhaw.parentwork.editor	Prisner, Thomas	-
zhaw.publication.status	publishedVersion	de_CH
zhaw.publication.review	Peer review (Abstract)	de_CH
zhaw.title.proceedings	EUROMAR 2022 Abstractbook	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

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Schmid, N., Bruderer, S., Fischetti, G., Paruzzo, F., Toscano, G., Graf, D., Fey, M., Henrici, A., Grabner, H., Wegner, J. D., Sigel, R. K. O., Heitmann, B., & Wilhelm, D. (2022). Deconvolution of NMR spectra : a deep learning-based approach [Conference poster]. In T. Prisner (Ed.), EUROMAR 2022 Abstractbook (p. 242). ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-27336

Schmid, N. et al. (2022) ‘Deconvolution of NMR spectra : a deep learning-based approach’, in T. Prisner (ed.) EUROMAR 2022 Abstractbook. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, p. 242. Available at: https://doi.org/10.21256/zhaw-27336.

N. Schmid et al., “Deconvolution of NMR spectra : a deep learning-based approach,” in EUROMAR 2022 Abstractbook, Jul. 2022, p. 242. doi: 10.21256/zhaw-27336.

SCHMID, Nicolas, Simon BRUDERER, Giulia FISCHETTI, Federico PARUZZO, Giuseppe TOSCANO, Dominik GRAF, Michael FEY, Andreas HENRICI, Helmut GRABNER, Jan Dirk WEGNER, Roland K. O. SIGEL, Björn HEITMANN und Dirk WILHELM, 2022. Deconvolution of NMR spectra : a deep learning-based approach. In: Thomas PRISNER (Hrsg.), EUROMAR 2022 Abstractbook [online]. Conference poster. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 13 Juli 2022. S. 242. Verfügbar unter: https://euromar2022.org/wp-content/uploads/2022/07/Euromar-AbstractBook_2022-A4_22jul_new.pdf

Schmid, Nicolas, Simon Bruderer, Giulia Fischetti, Federico Paruzzo, Giuseppe Toscano, Dominik Graf, Michael Fey, et al. 2022. “Deconvolution of NMR Spectra : A Deep Learning-Based Approach.” Conference poster. In EUROMAR 2022 Abstractbook, edited by Thomas Prisner, 242. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-27336.

Schmid, Nicolas, et al. “Deconvolution of NMR Spectra : A Deep Learning-Based Approach.” EUROMAR 2022 Abstractbook, edited by Thomas Prisner, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2022, p. 242, https://doi.org/10.21256/zhaw-27336.