1H nuclear magnetic resonance-based metabolomic characterization of wines by grape varieties and production areas

Correlation between sensory evaluation scores of Japanese sake and metabolome profiles

The aim of this study was to explore the association between taste and metabolite profiles of Japanese refined sake. Nontarget metabolome analysis was conducted using capillary electrophoresis mass spectrometry. Zatsumi, an unpleasant not clear flavor, and sweetness, bitterness, and sourness were graded by four experienced panelists. Regression models based on support vector regression (SVR) were used to estimate the relationships among sensory evaluation scores and quantified metabolites and visualized as a nonlinear relationship between sensory scores and metabolite components. The SVR model was highly accurate and versatile: the correlation coefficients for whole training data, cross-validation, and separated validation data were 0.86, 0.73, and 0.73, respectively, for zatsumi. Other sensory scores were also analyzed and modeled by SVR. The methodology demonstrated here carries great potential for predicting the relevant parameters and quantitative relationships between charged metabolites and sensory evaluation in Japanese refined sake.

Metabolomic insight into soy sauce through (1)H NMR spectroscopy

Soy sauce, a well-known seasoning in Asia and throughout the world, consists of many metabolites that are produced during fermentation or aging and that have various health benefits. However, their comprehensive assessment has been limited due to targeted or instrumentally specific analysis. This paper presents for the first time a metabolic characterization of soy sauce, especially that aged up to 12 years, to obtain a global understanding of the metabolic variations through (1)H NMR spectroscopy coupled with multivariate pattern recognition techniques. Elevated amino acids and organic acids and the consumption of carbohydrate were associated with continuous involvement of microflora in aging for 12 years. In particular, continuous increases in the levels of betaine were found during aging for up to 12 years, demonstrating that microbial- or enzyme-related metabolites were also coupled with osmotolerant or halophilic bacteria present during aging. This work provides global insights into soy sauce through a (1)H NMR-based metabolomic approach that enhances the current understanding of the holistic metabolome and allows assessment of soy sauce quality.

Metabolomic characterization of malolactic fermentation and fermentative behaviors of wine yeasts in grape wine

Wine contains a number of metabolites that are produced during alcoholic and malolactic fermentations (MLF) or aging, which are important compounds for determining wine quality. This study investigated changes in metabolites in wines to characterize malolactic fermentation (MLF) and to assess fermentative behaviors of wine yeast strains using (1)H nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistics. Principal component analysis (PCA) showed clear differentiation between non- and induced-malolactic fermented wines by wine lactic acid bacteria (LAB) and between wines fermented with various wine yeast strains. Metabolites such as glycerol, lactate, 2,3-butanediol, succinate, leucine, isoleucine, alanine, valine, proline, choline, gamma-aminobutyric acid (GABA), and polyphenols contributed to the differentiations. Decreased levels of malate and citrate along with increased levels of lactate were the metabolites most responsible for the differentiation of induced-MLF wines from non-MLF wines. In particular, high succinate levels provided evidence of an inhibitory effect of Saccharomyces bayanus against spontaneous MLF. Furthermore, dependence of metabolites on wine yeast strains was observed, demonstrating their different fermentative behaviors. This study demonstrates that wine fermentation by yeast and LAB can be characterized through global and multivariate statistical analysis of (1)H NMR spectral data.

Characterization of fermentative behaviors of lactic acid bacteria in grape wines through 1H NMR- and GC-based metabolic profiling

The effects of five commercial Oenococcus oeni strains (MCW, Enoferm alpha, Wyeast, Vinibacti111, and Vinibacti222) on fermentative behaviors, and variations of metabolites in Meoru ( Vitis coigneties ) wines during malolactic fermentation (MLF) were investigated by metabolomic analysis of (1)H NMR and GC data sets. In the development of MLF with various O. oeni strains, the fastest conversions of malic acid to lactic acid occurred in wines fermented with Enoferm alpha and Vinibacti111 strains. Seventeen primary metabolites and 65 secondary metabolites of volatile compounds in the wines were identified by (1)H NMR spectroscopy and GC-MS, respectively. In pattern recognition models of principal component analysis (PCA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA), significant differentiations between wines with O. oeni strains were identified by the secondary metabolites rather than by the primary metabolites, showing the effects of O. oeni strains only on the secondary metabolites. Twelve volatile compounds, 2-phenylethanol, isoamyl alcohol, 2-butanol, ethyl octanoate, ethyl hexanoate, hexadecanoic acid, diethyl succinate, butyl butyrate, octanoic acid, 9-hexadecanoic acid, isobutyric acid, and 2-ethyl-1-hexanol, contributed to the differentiation of wines according to O. oeni strain, including spontaneous MLF. This study demonstrates that O. oeni strains affect the secondary metabolites, which are easily identified through multivariate statistical analysis of GC-MS data set.

A thorough study on the use of quantitative 1H NMR in Rioja red wine fermentation processes

In this study, we focused our attention on monitoring the levels of important metabolites of wine during the alcoholic and malolactic fermentation processes by quantitative nuclear magnetic resonance (qNMR). Therefore, using (1)H NMR, the method allows the simultaneous quantification of ethanol, acetic, malic, lactic, and succinic acids, and the amino acids proline and alanine, besides the ratio proline/arginine through fermentation of must of grapes corresponding to the Tempranillo variety. Each (1)H NMR spectrum gives direct and visual information concerning these metabolites, and the effectiveness of each process was assessed and compared by carrying out analyses using infrared spectroscopy to ethanol and acetic acid. The quantitative data were explained with the aid of chemometric algorithms.

(1)H NMR-based metabolomic approach for understanding the fermentation behaviors of wine yeast strains

(1)H NMR spectroscopy coupled with multivariate statistical analysis was used for the first time to investigate metabolic changes in musts during alcoholic fermentation and wines during aging. Three Saccharomyces cerevisiae yeast strains (RC-212, KIV-1116, and KUBY-501) were also evaluated for their impacts on the metabolic changes in must and wine. Pattern recognition (PR) methods, including PCA, PLS-DA, and OPLS-DA scores plots, showed clear differences for metabolites among musts or wines for each fermentation stage up to 6 months. Metabolites responsible for the differentiation were identified as valine, 2,3-butanediol (2,3-BD), pyruvate, succinate, proline, citrate, glycerol, malate, tartarate, glucose, N-methylnicotinic acid (NMNA), and polyphenol compounds. PCA scores plots showed continuous movements away from days 1 to 8 in all musts for all yeast strains, indicating continuous and active fermentation. During alcoholic fermentation, the highest levels of 2,3-BD, succinate, and glycerol were found in musts with the KIV-1116 strain, which showed the fastest fermentation or highest fermentative activity of the three strains, whereas the KUBY-501 strain showed the slowest fermentative activity. This study highlights the applicability of NMR-based metabolomics for monitoring wine fermentation and evaluating the fermentative characteristics of yeast strains.