Comparison of Gas Chromatography-Coupled Time-of-Flight Mass Spectrometry and¹H Nuclear Magnetic Resonance Spectroscopy Metabolite Identification in White Wines from a Sensory Study Investigating Wine Body

Abstract

Metabolite profiles of white wines, including Chardonnay, Pinot gris, Riesling, Sauvignon blanc, and Viognier varieties, were determined using both gas chromatography-coupled time-of-flight mass spectrometry (GC-TOF-MS) and proton nuclear magnetic resonance spectroscopy ((1)H NMR). A total of 108 metabolites were identified by GC-TOF-MS, and 51 metabolites were identified by (1)H NMR; the majority of metabolites identified include the most abundant compounds found in wine (ethanol, glycerol, sugars, organic acids, and amino acids). Compositional differences in these wines correlating to the wine sensory property "body", or viscous mouthfeel, as scored by a trained panel were identified using partial least-squares (PLS) regression. Independently calculated GC-TOF-MS and NMR-based PLS models demonstrate potential for predictive models to replace expensive, time-consuming sensory panels. At the modeling stage, correlations between the measured and predicted values have coefficients of determination of 0.83 and 0.75 for GC-TOF-MS and (1)H NMR, respectively. Additionally, the MS- and NMR-based models present new insights into the chemical basis for wine mouthfeel properties.

Origins of Grape and Wine Aroma. Part 2. Chemical and Sensory Analysis

“…Recent examples include identification of sensory attributes that drive consumer and expert acceptance of Shiraz and Cabernet Sauvignon wines (Lattey et al 2010), assessment of the relationship between sensory and chemical data for oak-derived compounds found in French and Spanish wines (Prida and Chatonnet 2010), modeling of wine mouthfeel attributes using metabolomic data (Skogerson et al 2009), comparisons between vine vigor status with tannin and sensory data (Cortell et al 2008), assessment of the relationships between red wine textural characteristics and the chemical composition of Shiraz wines (Gawel et al 2007), comparisons of volatile components of sweet Fiano wines and sensory data (Genovese et al 2007), and comparisons between volatile compositional data and sensory data of Chardonnay, Cabernet Sauvignon, Malbec, and Sauvignon blanc wines made in different styles and/or from different regions (Lee and Noble 2006, Robinson et al 2011a, Benkwitz et al 2012, Hjelmeland et al 2013, King et al 2014. Future work may also consider aspects of wine and food interactions, given that an understanding of how people enjoy pairing wine with food combinations is commonly discussed in the popular literature (MadrigalGalan and Heymann 2006).…”

Multivariate statistics and the enactment of metabolic complexity

“…However, they were also playfully interested in finding biomarkers for the best tasting cheeses or the geographical origin of juices (Bruker Biospin, 2013). In doing so, they drew on a range of metabolomics research devoted to assuring the quality of commercially produced food products (Cajka et al, 2011;Gougeon et al, 2009;Skogerson et al, 2009), and also described their interest in finding biomarkers for embodied perceptions like 'gastronomical pleasure' and 'tastiness'. Such comments, although not altogether serious, reflect the ways in which metabolomics research gives rise to new, technologically mediated ways of investigating -and finding statistical correlations for -the complexity of metabolism, biology, and life.…”

NMR‐based metabolomics in wine science

“…Therefore, the 2D NMR experiments, such as COSY, TOCSY, and HMBC, are recommended to ensure the identification or assignment for providing a correct information. For example, proline of amino acids in wine gives us an important information on its relation with climate in vineyard and perceived viscous mouthfeel [8,10,15,16], and therefore, the assignment would be confirmed by 2D experiments. In particular, several NMR peaks of proton from same metabolites would be resolved at different spectral positions with proton chemical shift (ppm, d) in the 1 H NMR spectra, in which the number of the NMR peaks depends on the chemical structure and composition of the metabolites.…”