Quantification of Organic and Amino Acids in Beer by 1H NMR Spectroscopy
The quantification of organic and amino acids in beer using 1H NMR spectroscopy is demonstrated. Quantification was made both by integration of signals in the spectra together with use of calibration references and by use of partial least-squares (PLS) regression. Results from the NMR quantification...
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Published in: | Analytical chemistry (Washington) Vol. 76; no. 16; pp. 4790 - 4798 |
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Main Authors: | , , |
Format: | Journal Article |
Language: | English |
Published: |
Washington, DC
American Chemical Society
15-08-2004
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Subjects: | |
Online Access: | Get full text |
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Summary: | The quantification of organic and amino acids in beer using 1H NMR spectroscopy is demonstrated. Quantification was made both by integration of signals in the spectra together with use of calibration references and by use of partial least-squares (PLS) regression. Results from the NMR quantifications were compared with those obtained from determinations by amino acid analysis on HPLC and organic acid analysis by capillary electrophoresis. The described NMR-based methods could satisfactorily be used for quantification of several of the investigated metabolites in beer down to ∼10 mg/L and for most with a good to high accuracy compared to results obtained by HPLC and capillary electrophoresis (R 2 0.90−0.99). This was achieved with a simple sample preparation and one-dimensional 1H NMR spectra obtained in a few minutes. The use of PLS clearly improves the accuracy of the quantifications, based on comparison to results obtained by HPLC and capillary electrophoresis, and furthermore permits the determination of components with partially overlapped signals in the spectrum. NMR spectroscopy in combination with PLS will be a useful tool for the quantification of metabolites, not only in beer but also in other beverages and biofluids. |
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Bibliography: | ark:/67375/TPS-KDPLMXFH-T istex:75D297B55D75DCEAD6D6A26842557915276027A4 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0003-2700 1520-6882 |
DOI: | 10.1021/ac0496852 |