Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 454944
Title Automated quantum mechanical total line shape fitting model for quantitative NMR-based profiling of human serum metabolites
Author(s) Mihaleva, V.; Korhonen, S.P.; Duynhoven, J.P.M. van; Niemitz, M.; Vervoort, J.J.M.; Jacobs, D.M.
Source Analytical and Bioanalytical Chemistry 406 (2014)13. - ISSN 1618-2642 - p. 3091 - 3102.
Department(s) Biophysics
Publication type Refereed Article in a scientific journal
Publication year 2014
Keyword(s) h-1-nmr spectra - metabolomics - spectroscopy - quantification - deconvolution
Abstract An automated quantum mechanical total line shape (QMTLS) fitting model was implemented for quantitative nuclear magnetic resonance (NMR)-based profiling of 42 metabolites in ultrafiltrated human serum samples. Each metabolite was described by a set of chemical shifts, J-couplings, and line widths. These parameters were optimized for each metabolite in each sample by iteratively minimizing the difference between the calculated and the experimental spectrum. In total, 92.0 to 98.1 % of the signal intensities in the experimental spectrum could be explained by the calculated spectrum. The model was validated by comparison to signal integration of metabolites with isolated signals and by means of standard additions. Metabolites present at average concentration higher than 50 µM were quantified with average absolute relative error less than 10 % when using different initial parameters for the fitting procedure. Furthermore, the biological applicability of the QMTLS model was demonstrated on 287 samples from an intervention study in 37 human volunteers undergoing an exercise challenge. Our automated QMTLS model was able to cope with the large dynamic range of metabolite concentrations in serum and proved to be suitable for high-throughput analysis.
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