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Peak fitting in 2D ^sup 1^H-^sup 13^C HSQC NMR spectra for metabolomic studies
Peak fitting in 2D ^sup 1^H-^sup 13^C HSQC NMR spectra for metabolomic studies
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Peak fitting in 2D ^sup 1^H-^sup 13^C HSQC NMR spectra for metabolomic studies
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Peak fitting in 2D ^sup 1^H-^sup 13^C HSQC NMR spectra for metabolomic studies
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Peak fitting in 2D ^sup 1^H-^sup 13^C HSQC NMR spectra for metabolomic studies
Peak fitting in 2D ^sup 1^H-^sup 13^C HSQC NMR spectra for metabolomic studies
Journal Article

Peak fitting in 2D ^sup 1^H-^sup 13^C HSQC NMR spectra for metabolomic studies

2010
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Overview
A modified Lorentzian distribution function is used to model peaks in two-dimensional (2D) ^sup 1^H-^sup 13^C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra. The model fit is used to determine accurate chemical shifts from genuine signals in complex metabolite mixtures such as blood. The algorithm can be used to extract features from a set of spectra from different samples for exploratory metabolomics. First a reference spectrum is created in which the peak intensities are given by the median value over all samples at each point in the 2D spectra so that ^sup 1^H-^sup 13^C correlations in any spectra are accounted for. The mathematical model provides a footprint for each peak in the reference spectrum, which can be used to bin the ^sup 1^H-^sup 13^C correlations in each HSQC spectrum. The binned intensities are then used as variables in multivariate analyses and those found to be discriminatory are rapidly identified by cross referencing the chemical shifts of the bins with a database of ^sup 13^C and ^sup 1^H chemical shift correlations from known metabolites.
Publisher
Springer Nature B.V

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