Abstract
1H NMR (nuclear magnetic resonance spectroscopy) has been used for metabolomic analysis of 'Riesling' and 'Mueller-Thurgau' white wines from the German Palatinate region. Diverse two-dimensional NMR techniques have been applied for the identification of metabolites, including phenolics. It is shown that sensory analysis correlates with NMR-based metabolic profiles of wine. 1H NMR data in combination with multivariate data analysis methods, like principal component analysis (PCA), partial least squares projections to latent structures (PLS), and bidirectional orthogonal projections to latent structures (O2PLS) analysis, were employed in an attempt to identify the metabolites responsible for the taste of wine, using a non-targeted approach. The high quality wines were characterized by elevated levels of compounds like proline, 2,3-butanediol, malate, quercetin, and catechin. Characterization of wine based on type and vintage was also done using orthogonal projections to latent structures (OPLS) analysis. 'Riesling' wines were characterized by higher levels of catechin, caftarate, valine, proline, malate, and citrate whereas compounds like quercetin, resveratrol, gallate, leucine, threonine, succinate, and lactate, were found discriminating for 'Mueller-Thurgau'. The wines from 2006 vintage were dominated by leucine, phenylalanine, citrate, malate, and phenolics, while valine, proline, alanine, and succinate were predominantly present in the 2007 vintage. Based on these results, it can be postulated the NMR-based metabolomics offers an easy and comprehensive analysis of wine and in combination with multivariate data analyses can be used to investigate the source of the wines and to predict certain sensory aspects of wine.
Original language | English |
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Pages (from-to) | 255-266 |
Number of pages | 12 |
Journal | Journal of Biomolecular NMR |
Volume | 49 |
Issue number | 3-4 |
DOIs | |
Publication status | Published - Apr 2011 |
Bibliographical note
Funding Information:Acknowledgments The authors greatfully acknowledge Stefan Hilz from Landwirtschaftskammer Rheinland-Pfalz and his team for providing samples of wines for NMR analysis and their sensorial classification. This work was done in the frame of the EraNET Genomic Research-Assisted breeding for Sustainable Production of Quality GRAPEs and WINE (GRASP) in coordination with Dr. Eva Zyprian (http://urgi.versailles.inra.fr/projects/GRASP/). The authors thank Higher Education Commission (HEC) of Pakistan for the financial support of Kashif Ali.
Keywords
- Multivariate data analysis
- Nuclear magnetic resonance metabolomics
- Phenolics
- Vintages
- Wine sensory attributes
- Wine types