Peak fitting in 2D 1H-13C HSQC NMR Spectra for Metabolomic Studies

James S. McKenzie, Adrian J. Charlton, James A. Donarski, Alan D. MacNicholl, Julie C. Wilson

Research output: Contribution to journalArticlepeer-review


A modified Lorentzian distribution function is
used to model peaks in two-dimensional (2D) 1H–13C
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 1H–13C 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 1H–13C 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 13C and
1H chemical shift correlations from known metabolites
Original languageEnglish
Pages (from-to)574-582
Number of pages9
Issue number4
Publication statusPublished - Dec 2010


  • 2D 1H–13C HSQC NMR
  • Peak modelling
  • Lorentzian
  • Metabolomics
  • Classification
  • Metabolites

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