NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm

We introduce NMR-Onion, an open-source, computationally efficient algorithm based on Python and PyTorch, designed to facilitate the automatic deconvolution of 1D NMR spectra. NMR-Onion features two innovative time-domain models capable of handling asymmetric non-Lorentzian line shapes. Its core components for resolution-enhanced peak detection and digital filtering of user-specified key regions ensure precise peak prediction and efficient computation. The NMR-Onion framework includes three built-in statistical models, with automatic selection via the BIC criterion. Additionally, NMR-Onion assesses the repeatability of results by evaluating post-modeling uncertainty. Using the NMR-Onion algorithm helps to minimize excessive peak detection.

Authors

Institutions

Publication Details

Journal
Heliyon
Published
2024-08-30
DOI
https://doi.org/10.1016/j.heliyon.2024.e36998
Citations
2
Primary Topic
NMR spectroscopy and applications
Type
article
Field-Weighted Citation Impact
1.32

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm

Rasmus Bro, Charlotte H. Gotfredsen, Mikael Lenz Strube, Michael Riis Andersen et al.
2 citations
Heliyon
NMR spectroscopy and applications
1.32
article

NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm

Rasmus Bro, Charlotte H. Gotfredsen, Mikael Lenz Strube, Michael Riis Andersen, Mette-Maya Siewertsen, Mathies Brinks Sørensen
article en
2 citations

Abstract

We introduce NMR-Onion, an open-source, computationally efficient algorithm based on Python and PyTorch, designed to facilitate the automatic deconvolution of 1D NMR spectra. NMR-Onion features two innovative time-domain models capable of handling asymmetric non-Lorentzian line shapes. Its core components for resolution-enhanced peak detection and digital filtering of user-specified key regions ensure precise peak prediction and efficient computation. The NMR-Onion framework includes three built-in statistical models, with automatic selection via the BIC criterion. Additionally, NMR-Onion assesses the repeatability of results by evaluating post-modeling uncertainty. Using the NMR-Onion algorithm helps to minimize excessive peak detection.

HeliyonVol. 10(17)
University of Copenhagen (DK), Technical University of Denmark (DK)
Villum Fonden, National Research Foundation, Danmarks Grundforskningsfond
Openalex Percentile: Top 11%
NMR spectroscopy and applications
1.32
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.