Food Metabolomics Evaluation of Japanese Rice Wine Types Using LC/MS/MS

Importance of the Topic

Food metabolomics provides a molecular understanding of food quality and flavor by profiling small molecules.
In sake evaluation, this approach complements traditional sensory tests by quantifying functional and flavor-related metabolites.

Objectives and Study Overview

This study aimed to differentiate five specially designated Japanese rice wine types based on their hydrophilic metabolite profiles.
These sake samples share ingredients but differ in rice polishing ratios and yeast strains, enabling systematic comparison.

Methodology and Instrumentation

Samples were prepared by centrifugation and ultrafiltration to isolate aqueous metabolites.
Analysis employed Shimadzu LCMS-8060 coupled to a Nexera X2 HPLC using a free ion-pair LC/MS/MS method for primary metabolites.
HPLC used a reversed-phase column with 0.1% formic acid in water and acetonitrile under gradient elution at 0.25 mL/min.
MS detection utilized ESI in both positive and negative modes with MRM transitions, optimized gas flows and temperatures.
Multivariate analysis included principal component analysis performed in SIMCA software on area ratios to internal standards.

Main Results and Discussion

PCA score plots distinctly separated junmai-daiginjoshu from junmaishu and unfiltered origarami vs clear types.
Primary component loadings showed junmaishu enriched in amino acids including glutamic acid, leucine, threonine, isoleucine and serine, linked to umami taste.
Junmai-daiginjoshu exhibited higher levels of organic acids such as malic acid and pyruvic acid, suggesting a lighter, refreshing flavor.
Secondary component analysis revealed yeast strain effects, with variations in antioxidant and organic acid levels including glutathione, citric acid and lactic acid.

Benefits and Practical Applications

The method provides objective metabolic markers for sake classification, reducing reliance on subjective sensory panels.
Quantitative metabolite data support quality control, product differentiation and prediction of flavor attributes.
Identification of bioactive compounds like glutathione offers insights into potential health benefits and functional labeling.

Future Trends and Potential Applications

Integration with sensory evaluation and machine learning could enhance predictive models of taste and quality.
Expansion to broader metabolome coverage and high-throughput workflows will accelerate food product development.
Application to other fermented beverages and foods promises standardized, data-driven quality assessment across the industry.

Conclusion

Comprehensive LC/MS/MS metabolomics effectively characterizes sake types by linking metabolite profiles to manufacturing variables.
This approach fosters scientific understanding of flavor and functionality, paving the way for advanced food quality analysis.