Articles | Volume 1, issue 2
https://doi.org/10.5194/mr-1-141-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/mr-1-141-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jul 2020
Research article |
| 02 Jul 2020
| 02 Jul 2020
Improving the accuracy of model-based quantitative nuclear magnetic resonance
Yevgen Matviychuk
CORRESPONDING AUTHOR
Department of Chemical and Process Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
Ellen Steimers
Lehrstuhl für Thermodynamik, Technische Universität Kaiserslautern, Erwin-Schrödinger-Straße 44, Kaiserslautern 67663, Germany
Erik von Harbou
Lehrstuhl für Thermodynamik, Technische Universität Kaiserslautern, Erwin-Schrödinger-Straße 44, Kaiserslautern 67663, Germany
current address: BASF SE, Research and Development, Ludwigshafen, Germany
Daniel J. Holland
Department of Chemical and Process Engineering, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand
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Cited
16 citations as recorded by crossref.
- A Monte Carlo Method for Analyzing Systematic and Random Uncertainty in Quantitative Nuclear Magnetic Resonance Measurements G. Khirich https://doi.org/10.1021/acs.analchem.1c00407
- Accurate measurements of self‐diffusion coefficients with benchtop NMR using a QM model‐based approach E. Steimers et al. https://doi.org/10.1002/mrc.5300
- Advancing quantitative NMR for high-precision isotopic analysis with rnmrfit 2.0 K. Isaac et al. https://doi.org/10.1016/j.jmr.2025.107991
- Reconstruction of the local electrostatic force field enables ultrafast lithium-ion conduction in Li2ZrCl6 X. Yang et al. https://doi.org/10.1016/j.esci.2026.100665
- Calibration‐Free Chemical Process and Quality Control Units as Enablers for Modular Production M. Bornemann‐Pfeiffer et al. https://doi.org/10.1002/cite.202000150
- Deep set model for the automated NMR fingerprinting of unknown mixtures J. Wagner et al. https://doi.org/10.1039/D5DD00490J
- Spectral and chromatographic analysis of food-related components produced by bacteria, plants, and fungi. A review F. Granados-Chinchilla https://doi.org/10.1016/j.microc.2023.109499
- Improved peak-parameter estimation in overlapping 1D NMR spectra through a joint-refinement extension of Global Spectral Deconvolution C. Cobas https://doi.org/10.1016/j.aca.2026.346401
- Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few in Vivo Data D. Chen et al. https://doi.org/10.1109/TCI.2023.3267623
- NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm M. Brinks Sørensen et al. https://doi.org/10.1016/j.heliyon.2024.e36998
- Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring K. Hara et al. https://doi.org/10.3390/pr10071264
- Solubility of lithium, nickel, and cobalt oxides in deep eutectic solvents R. Rahman et al. https://doi.org/10.1016/j.molliq.2025.128875
- Rational method for defining and quantifying pseudo-components based on NMR spectroscopy T. Specht et al. https://doi.org/10.1039/D3CP00509G
- Investigation of Reaction Kinetics of the Selective Hydrogenation of a Terminal Alkyne Under Industrially Relevant Conditions With Benchtop NMR Spectroscopy P. Sterner et al. https://doi.org/10.1002/mrc.70091
- Automated nuclear magnetic resonance fingerprinting of mixtures T. Specht et al. https://doi.org/10.1002/mrc.5381
- Application of Odd-Order Derivatives in Fourier Transform Nuclear Magnetic Resonance Spectroscopy toward Quantitative Deconvolution S. Chen et al. https://doi.org/10.1021/acsomega.4c04536
16 citations as recorded by crossref.
- A Monte Carlo Method for Analyzing Systematic and Random Uncertainty in Quantitative Nuclear Magnetic Resonance Measurements G. Khirich https://doi.org/10.1021/acs.analchem.1c00407
- Accurate measurements of self‐diffusion coefficients with benchtop NMR using a QM model‐based approach E. Steimers et al. https://doi.org/10.1002/mrc.5300
- Advancing quantitative NMR for high-precision isotopic analysis with rnmrfit 2.0 K. Isaac et al. https://doi.org/10.1016/j.jmr.2025.107991
- Reconstruction of the local electrostatic force field enables ultrafast lithium-ion conduction in Li2ZrCl6 X. Yang et al. https://doi.org/10.1016/j.esci.2026.100665
- Calibration‐Free Chemical Process and Quality Control Units as Enablers for Modular Production M. Bornemann‐Pfeiffer et al. https://doi.org/10.1002/cite.202000150
- Deep set model for the automated NMR fingerprinting of unknown mixtures J. Wagner et al. https://doi.org/10.1039/D5DD00490J
- Spectral and chromatographic analysis of food-related components produced by bacteria, plants, and fungi. A review F. Granados-Chinchilla https://doi.org/10.1016/j.microc.2023.109499
- Improved peak-parameter estimation in overlapping 1D NMR spectra through a joint-refinement extension of Global Spectral Deconvolution C. Cobas https://doi.org/10.1016/j.aca.2026.346401
- Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few in Vivo Data D. Chen et al. https://doi.org/10.1109/TCI.2023.3267623
- NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm M. Brinks Sørensen et al. https://doi.org/10.1016/j.heliyon.2024.e36998
- Parameter Visualization of Benchtop Nuclear Magnetic Resonance Spectra toward Food Process Monitoring K. Hara et al. https://doi.org/10.3390/pr10071264
- Solubility of lithium, nickel, and cobalt oxides in deep eutectic solvents R. Rahman et al. https://doi.org/10.1016/j.molliq.2025.128875
- Rational method for defining and quantifying pseudo-components based on NMR spectroscopy T. Specht et al. https://doi.org/10.1039/D3CP00509G
- Investigation of Reaction Kinetics of the Selective Hydrogenation of a Terminal Alkyne Under Industrially Relevant Conditions With Benchtop NMR Spectroscopy P. Sterner et al. https://doi.org/10.1002/mrc.70091
- Automated nuclear magnetic resonance fingerprinting of mixtures T. Specht et al. https://doi.org/10.1002/mrc.5381
- Application of Odd-Order Derivatives in Fourier Transform Nuclear Magnetic Resonance Spectroscopy toward Quantitative Deconvolution S. Chen et al. https://doi.org/10.1021/acsomega.4c04536
Saved (final revised paper)
Latest update: 05 Nov 2026
Short summary
Quantitative analysis of mixtures is a challenge in applications ranging from foods to industrial chemistry. Nuclear magnetic resonance (NMR) is increasingly used for analysis; however, overlapping peaks in the spectrum pose a major difficulty, especially for the new generation of benchtop NMR instruments. We propose a fast and simple model-based approach to enhance the accuracy of quantification. We demonstrate it on industrially relevant test samples where the accuracy is increased by 40 %.
Quantitative analysis of mixtures is a challenge in applications ranging from foods to...
