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

Importance of the Topic

Food metabolomics leverages comprehensive profiling of small molecules to assess food quality, authenticity and functionality. Applying this approach to Japanese rice wine (sake) enables objective evaluation of flavor, aroma and nutritional value, complementing traditional sensory tests. By linking metabolite data with production parameters, researchers can refine brewing processes, assure product consistency and discover bioactive compounds.

Study Objectives and Overview

This study aimed to distinguish five specially designated sake types produced by a single manufacturer using targeted metabolomics. The goals were:

• To profile hydrophilic metabolites across sake categories
• To identify characteristic compounds responsible for taste differences
• To correlate metabolic fingerprints with brewing methods and yeast strains

Methodology

Samples of five sake variants (including junmai-daiginjoshu, junmai-daiginjoshu with lees "origarami", junmaishu, junmaishu origarami and junmaishu brewed with an alternative yeast strain) were centrifuged and ultrafiltered to remove particles. The clear supernatant was analyzed for 97 key hydrophilic metabolites, encompassing amino acids, organic acids, nucleosides and nucleotides. Data processing involved principal component analysis (PCA) using SIMCA software to visualize compositional differences.

Used Instrumentation

The analysis employed a Shimadzu Nexera X2 liquid chromatograph coupled to an LCMS-8060 triple quadrupole mass spectrometer. Key parameters:

• Chromatography: reversed-phase column, 0.1% formic acid in water and acetonitrile, gradient elution, 0.25 mL/min flow, 3 μL injection
• Mass spectrometry: electrospray ionization in positive/negative mode, multiple reaction monitoring, optimized gas flows and temperatures (DL 250 °C, block heater 400 °C, interface 300 °C)

Main Results and Discussion

PCA score plots separated sake types by polishing ratio and yeast strain.

• Junmaishu samples showed elevated levels of amino acids (glutamic acid, leucine, threonine, isoleucine, serine), linked to pronounced umami taste.
• Junmai-daiginjoshu exhibited higher concentrations of organic acids (malic acid, pyruvic acid), suggesting a lighter, more refreshing profile.
• Lees-containing "origarami" variants contained increased amino acids, implying enhanced savory notes from rice and yeast residues.
• A secondary PCA highlighted that the alternative yeast strain in one junmaishu sample produced greater glutathione, citric acid and lactic acid levels, indicating potential antioxidant benefits.

Benefits and Practical Applications

This metabolomics workflow provides:

• Objective differentiation of sake types based on chemical markers
• Insights into how rice polishing and yeast choice shape taste and functionality
• Scientific basis for optimizing brewing to target desired flavor and health attributes

Future Trends and Applications

Advances in high-throughput mass spectrometry and data analytics will expand metabolite coverage and resolution. Integration with sensory panels and machine learning could enable predictive models of taste. Broader adoption of food metabolomics may drive innovation in fermentation industries, personalized nutrition and functional beverage development.

Conclusion

Targeted LC/MS/MS metabolomics effectively characterizes compositional differences among sake types, correlating specific metabolite patterns with production parameters. This approach supports quality control, product development and discovery of health-related compounds in fermented beverages.

References

Shimadzu Corporation. LC/MS/MS Method Package for Primary Metabolites Ver. 2 Application Note. Aug. 2019.
Sartorius Stedim Data Analytics AB. SIMCA Software Documentation.