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.

pyIHM: Indirect Hard Modeling, in Python / Bruno, Francesco; Fiorucci, Letizia; Vignoli, Alessia; Meyer, Klas; Maiwald, Michael; Ravera, Enrico. - In: ANALYTICAL CHEMISTRY. - ISSN 0003-2700. - STAMPA. - 97:(2025), pp. 4598-4605. [10.1021/acs.analchem.4c06484]

pyIHM: Indirect Hard Modeling, in Python

Bruno, Francesco
;
Fiorucci, Letizia;Vignoli, Alessia;Ravera, Enrico
2025

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.
2025
97
4598
4605
Goal 3: Good health and well-being
Bruno, Francesco; Fiorucci, Letizia; Vignoli, Alessia; Meyer, Klas; Maiwald, Michael; Ravera, Enrico
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1423524
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