We have recently
presented an Automated Quantification Algorithm
(AQuA) and demonstrated its utility for rapid and accurate absolute
metabolite quantification in
1
H NMR spectra in which positions
and line widths of signals were predicted from a constant metabolite
spectral library. The AQuA quantifies based on one preselected signal
per metabolite and employs library spectra to model interferences
from other metabolite signals. However, for some types of spectra,
the interspectral deviations of signal positions and line widths can
be pronounced; hence, interferences cannot be modeled using a constant
spectral library. We here address this issue and present an improved
AQuA that handles interspectral deviations. The improved AQuA monitors
and characterizes the appearance of specific signals in each spectrum
and automatically adjusts the spectral library to model interferences
accordingly. The performance of the improved AQuA was tested on a
large data set from plasma samples collected using ethylenediaminetetraacetic
acid (EDTA) as an anticoagulant (
n
= 772). These
spectra provided a suitable test system for the improved AQuA since
EDTA signals (i) vary in intensity, position, and line width between
spectra and (ii) interfere with many signals from plasma metabolites
targeted for quantification (
n
= 54). Without the
improvement, ca. 20 out of the 54 metabolites would have been overestimated.
This included acetylcarnitine and ornithine, which are considered
particularly difficult to quantify with
1
H NMR in EDTA-containing
plasma. Furthermore, the improved AQuA performed rapidly (<10 s
for all spectra). We believe that the improved AQuA provides a basis
for automated quantification in other data sets where specific signals
show interspectral deviations.
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