pyIHM: Indirect Hard Modeling, in Python

NMR is a powerful analytical technique that combines an exquisite qualitative power, related to the unicity of the spectra of each molecule in a mixture, with an intrinsic quantitativeness, related to the fact that the integral of each peak only depends on the number of nuclei (i.e., the amount of substance times the number of equivalent nuclei in the signal), regardless of the molecule. Signal integration is the most common approach in quantitative NMR but has several drawbacks (vide infra). An alternative is to use hard modeling of the peaks. In this paper, we present pyIHM, a Python package for the quantification of the components of NMR spectra through indirect hard modeling, and we discuss some numerical details of the implementation that make this approach robust and reliable.

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Publication Details

Journal
Analytical Chemistry
Published
2025-02-24
DOI
https://doi.org/10.1021/acs.analchem.4c06484
Citations
2
Primary Topic
NMR spectroscopy and applications
Type
article
Field-Weighted Citation Impact
3.74

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article

pyIHM: Indirect Hard Modeling, in Python

Michael Maiwald, Alessia Vignoli, Klas Meyer, F. Bruno et al.
2 citations
Analytical Chemistry
NMR spectroscopy and applications
3.74
article

pyIHM: Indirect Hard Modeling, in Python

Michael Maiwald, Alessia Vignoli, Klas Meyer, F. Bruno, Enrico Ravera, Letizia Fiorucci
article en
2 citations

Abstract

NMR is a powerful analytical technique that combines an exquisite qualitative power, related to the unicity of the spectra of each molecule in a mixture, with an intrinsic quantitativeness, related to the fact that the integral of each peak only depends on the number of nuclei (i.e., the amount of substance times the number of equivalent nuclei in the signal), regardless of the molecule. Signal integration is the most common approach in quantitative NMR but has several drawbacks (vide infra). An alternative is to use hard modeling of the peaks. In this paper, we present pyIHM, a Python package for the quantification of the components of NMR spectra through indirect hard modeling, and we discuss some numerical details of the implementation that make this approach robust and reliable.

Analytical ChemistryVol. 97(8)
Federal Institute For Materials Research and Testing (DE), Interuniversity Consortium for Magnetic Resonance (IT), University of Florence (IT)
Ministero dell'Università e della Ricerca, H2020 Research Infrastructures
Decent work and economic growth, Gender equality
Openalex Percentile: Top 9%
NMR spectroscopy and applications
3.74
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