pyIHM: Indirect Hard Modeling, in Python

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL Analytical Chemistry Pub Date : 2025-02-24 DOI:10.1021/acs.analchem.4c06484
Francesco Bruno, Letizia Fiorucci, Alessia Vignoli, Klas Meyer, Michael Maiwald, Enrico Ravera
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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.

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pyIHM: Python中的间接硬建模
核磁共振是一种强大的分析技术,它结合了精致的定性能力,与混合物中每个分子光谱的唯一性有关,与内在定量有关,与每个峰的积分仅取决于核的数量(即物质的数量乘以信号中等效核的数量),而与分子无关。信号集成是定量核磁共振中最常用的方法,但也存在一些缺陷(如红外光谱)。另一种方法是对峰值进行硬建模。在本文中,我们提出了pyIHM,一个通过间接硬建模来量化核磁共振光谱成分的Python包,我们讨论了一些实现的数值细节,使这种方法具有鲁棒性和可靠性。
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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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