Automatic classification of signal regions in 1H nuclear magnetic resonance spectra

Fischetti, Giulia; Schmid, Nicolas; Bruderer, Simon; Caldarelli, Guido; Scarso, Alessandro; Henrici, Andreas; Wilhelm, Dirk

doi:10.3389/frai.2022.1116416

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26698

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	Automatic classification of signal regions in 1H nuclear magnetic resonance spectra
Authors:	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
Published in:	Frontiers in Artificial Intelligence
Volume(Issue):	5
Issue:	1116416
Issue Date:	11-Jan-2023
Publisher / Ed. Institution:	Frontiers Research Foundation
ISSN:	2624-8212
Language:	English
Subjects:	Nuclear magnetic resonance; Automatic signal classification; Deep learning; 1H spectra; Multiplet assignment
Subject (DDC):	006: Special computer methods 530: Physics
Abstract:	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
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)
Published as part of the ZHAW project:	Maschinelles Lernen für NMR-Spektroskopie
Appears in collections:	Publikationen School of Engineering

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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.