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Article

Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis

1
Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764 Neuherberg, Germany
2
Chair of Analytical Food Chemistry, Technical University Munich, D-85354 Freising-Weihenstephan, Germany
3
German Center for Diabetes Research, Ingolstädter Landstraße 1, D-85764 Neuherberg, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Helen G. Gika
Metabolites 2021, 11(5), 285; https://doi.org/10.3390/metabo11050285
Received: 22 March 2021 / Revised: 26 April 2021 / Accepted: 27 April 2021 / Published: 29 April 2021
(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)
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. View Full-Text
Keywords: NMR; metabolomics; data processing; voigt-fitting NMR; metabolomics; data processing; voigt-fitting
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MDPI and ACS Style

Haslauer, K.E.; Schmitt-Kopplin, P.; Heinzmann, S.S. Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis. Metabolites 2021, 11, 285. https://doi.org/10.3390/metabo11050285

AMA Style

Haslauer KE, Schmitt-Kopplin P, Heinzmann SS. Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis. Metabolites. 2021; 11(5):285. https://doi.org/10.3390/metabo11050285

Chicago/Turabian Style

Haslauer, Kristina E., Philippe Schmitt-Kopplin, and Silke S. Heinzmann. 2021. "Data Processing Optimization in Untargeted Metabolomics of Urine Using Voigt Lineshape Model Non-Linear Regression Analysis" Metabolites 11, no. 5: 285. https://doi.org/10.3390/metabo11050285

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