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Magnetic Resonance An interactive open-access publication of the Groupement AMPERE
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https://doi.org/10.5194/mr-2019-4
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/mr-2019-4
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 28 Jan 2020

Submitted as: research article | 28 Jan 2020

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This preprint is currently under review for the journal MR.

Improving the Accuracy of Model-based Quantitative NMR

Yevgen Matviychuk1, Ellen Steimers2, Erik von Harbou2,a, and Daniel J. Holland1 Yevgen Matviychuk et al.
  • 1University of Canterbury, Private Bag 4800, Cristchurch 8140, New Zealand
  • 2Technische Universität Kaiserslautern, Erwin-Schrödinger-Straße 44, Kaiserslautern 67663, Germany
  • acurrent address: BASF SE, Research and Development, Ludwigshafen, Germany

Abstract. We proposed an effective and computationally simple mechanism to improve the accuracy of model-based quantification in NMR data analysis. The proposed adjustment procedure aims to account for all useful signal left in the residual after the usual least squares fit, which can signify a case of model misspecification – a problem notoriously difficult to avoid in most model-based qNMR methods. Our alternative optimization criterion explicitly relies on the denoising of residual and smoothing the remaining baseline and is particularly effective in correcting errors in spectrum phasing. The results of analysis of experimental datasets obtained with high and medium field spectrometers indicate the accuracy improvement by 20–40 % compared to the usual least-squares model fit.

Yevgen Matviychuk et al.

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Yevgen Matviychuk et al.

Yevgen Matviychuk et al.

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Latest update: 02 Apr 2020
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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 the 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 analysis 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...
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