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Data processing optimization in untargeted metabolomics of urine using Voigt lineshape model non-linear regression analysis.

Metabolites 11:285 (2021)
Publ. Version/Full Text DOI
Open Access Gold
Creative Commons Lizenzvertrag
Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine 1H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Nmr ; Data Processing ; Metabolomics ; Voigt-fitting; Quantification; Metabolites; Efficient
ISSN (print) / ISBN 2218-1989
e-ISSN 2218-1989
Journal Metabolites
Quellenangaben Volume: 11, Issue: 5, Pages: , Article Number: 285 Supplement: ,
Publisher MDPI
Publishing Place St Alban-anlage 66, Ch-4052 Basel, Switzerland
Reviewing status Peer reviewed
Grants Deutsche Forschungsgemeinschaft (DFG)