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Issue 18, 2018
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Comparison of computational approaches for identification and quantification of urinary metabolites in 1H NMR spectra

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Abstract

Nuclear magnetic resonance (NMR) spectroscopy is extensively used in analytical chemistry as a powerful, non-invasive, and non-destructive tool to elucidate detailed structures of small molecules in complex mixtures. A major initiative in NMR is the identification of metabolic changes in biological fluids, particularly urine, as potential biomarkers for specific diseases or occupational exposure. However, major challenges are encountered during data processing of complex NMR spectra, presenting obstacles in the use of NMR analysis in clinical applications. In this report, metabolite concentrations were determined using three different computational approaches with complex NMR spectra obtained using 33 replicates of quality control (QC) human urine samples. We have used a new computational method involving Monte Carlo (MC) simulation to automatically deconvolve and quantify metabolites in NMR spectra from human urine. MC simulation is independent of experimental bias or human error, and is recommended as the least biased approach to peak fitting for NMR spectra derived from human urine samples. We found that similar results could be obtained using MC simulation in urine samples compared with two previous approaches that are subject to experimental bias and/or human error.

Graphical abstract: Comparison of computational approaches for identification and quantification of urinary metabolites in 1H NMR spectra

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Publication details

The article was received on 12 Apr 2018, accepted on 14 Apr 2018 and first published on 18 Apr 2018


Article type: Paper
DOI: 10.1039/C8AY00830B
Citation: Anal. Methods, 2018,10, 2129-2137
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    Comparison of computational approaches for identification and quantification of urinary metabolites in 1H NMR spectra

    M. Cassiède, P. Mercier, P. R. Shipley, M. Dueck, S. Kamravaei, S. Nair, J. Mino, L. Pei, D. Broadhurst, P. Lacy and B. Quémerais, Anal. Methods, 2018, 10, 2129
    DOI: 10.1039/C8AY00830B

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