Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-29538
Publication type: | Conference poster |
Type of review: | Peer review (abstract) |
Title: | Uncertainty quantification for reliable automatic multiplet classification in 1H NMR spectra |
Authors: | Fischetti, Giulia Schmid, Nicolas Henrici, Andreas Wilhelm, Dirk Bruderer, Simon Heitmann, Bjoern Scarso, Alessandro Caldarelli, Guido |
et. al: | No |
DOI: | 10.21256/zhaw-29538 |
Proceedings: | Euromar 2023 Programme & Abstract Book |
Editors of the parent work: | Prisner, Thomas |
Page(s): | 350 |
Conference details: | European Conference on Magnetic Resonance (EUROMAR), Glasgow, United Kingdom, 9-13 July 2023 |
Issue Date: | Jul-2023 |
Publisher / Ed. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
Language: | English |
Subjects: | NMR spectroscopy; Machine learning; Deep learning; Uncertainty quantification |
Subject (DDC): | 006: Special computer methods 530: Physics |
Abstract: | Proton NMR is the fastest and most straightforward of all NMR experimental designs. Unfortunately, it suffers from lengthy annotation times and does not always have a clear and unbiased interpretation. Introducing an automatic procedure for the analysis of NMR data that can ease the chemical compounds characterization while ensuring consistency of the results across the scientific community is still an open challenge. Recently, we introduced a supervised deep learning model that performs automated classification of signal regions for their coupling pattern. Here we show how including uncertainty quantification in deep learning frameworks applied to NMR serves a dual-purpose of increasing the reliability of the prediction and detecting overlapping multiplets. |
URI: | https://digitalcollection.zhaw.ch/handle/11475/29538 |
Fulltext version: | Published version |
License (according to publishing contract): | Licence according to publishing contract |
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 |
Files in This Item:
File | Description | Size | Format | |
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2023_Fischetti-etal_Uncertainty-quantification-multiplet-classification_EUROMAR2023-Poster.pdf | 2.9 MB | Adobe PDF | View/Open |
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Fischetti, G., Schmid, N., Henrici, A., Wilhelm, D., Bruderer, S., Heitmann, B., Scarso, A., & Caldarelli, G. (2023). Uncertainty quantification for reliable automatic multiplet classification in 1H NMR spectra [Conference poster]. In T. Prisner (Ed.), Euromar 2023 Programme & Abstract Book (p. 350). ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29538
Fischetti, G. et al. (2023) ‘Uncertainty quantification for reliable automatic multiplet classification in 1H NMR spectra’, in T. Prisner (ed.) Euromar 2023 Programme & Abstract Book. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, p. 350. Available at: https://doi.org/10.21256/zhaw-29538.
G. Fischetti et al., “Uncertainty quantification for reliable automatic multiplet classification in 1H NMR spectra,” in Euromar 2023 Programme & Abstract Book, Jul. 2023, p. 350. doi: 10.21256/zhaw-29538.
FISCHETTI, Giulia, Nicolas SCHMID, Andreas HENRICI, Dirk WILHELM, Simon BRUDERER, Bjoern HEITMANN, Alessandro SCARSO und Guido CALDARELLI, 2023. Uncertainty quantification for reliable automatic multiplet classification in 1H NMR spectra. In: Thomas PRISNER (Hrsg.), Euromar 2023 Programme & Abstract Book. Conference poster. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Juli 2023. S. 350
Fischetti, Giulia, Nicolas Schmid, Andreas Henrici, Dirk Wilhelm, Simon Bruderer, Bjoern Heitmann, Alessandro Scarso, and Guido Caldarelli. 2023. “Uncertainty Quantification for Reliable Automatic Multiplet Classification in 1H NMR Spectra.” Conference poster. In Euromar 2023 Programme & Abstract Book, edited by Thomas Prisner, 350. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29538.
Fischetti, Giulia, et al. “Uncertainty Quantification for Reliable Automatic Multiplet Classification in 1H NMR Spectra.” Euromar 2023 Programme & Abstract Book, edited by Thomas Prisner, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2023, p. 350, https://doi.org/10.21256/zhaw-29538.
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