Food Metabolomics Analysis of Deterioration Characteristics of Alcoholic Drinks Using LC/MS/MS

Importance of the Topic

Food metabolomics applies advanced LC/MS/MS techniques to monitor biochemical changes in food products. By profiling hydrophilic metabolites, researchers can assess quality, predict spoilage, and identify functional compounds more objectively than traditional sensory tests.

Objectives and Study Overview

This study investigated how accelerated deterioration conditions affect key metabolites in Japanese rice wines (sake) and white wine. Samples were stored under four regimes—refrigeration, light exposure at room temperature, heat challenge, and agitation—and then analyzed to identify markers of quality loss.

Methodology and Instrumentation

Samples were centrifuged, diluted, and measured using Shimadzu’s Nexera X2 UHPLC coupled with LCMS-8060 in both positive and negative ESI modes, employing an ion-pairing–free method for 97 primary hydrophilic metabolites.

• HPLC: Reversed-phase column; gradient elution with 0.1% formic acid in water and acetonitrile; flow rate 0.25 mL/min; injection volume 3 µL.
• MS: MRM mode; nebulizing gas 3.0 L/min; drying gas 10.0 L/min; block heater 400 °C; interface 300 °C.

Main Results and Discussion

Principal component analysis showed little overall separation, but ANOVA revealed significant metabolite changes under light-exposed storage (Condition B). Key findings:

• Methionine sulfoxide levels rose markedly in all beverages under light exposure, suggesting its utility as an oxidation marker.
• Uric acid decreased in sake but remained stable in white wine (protected by added sulfite), highlighting its antioxidant role.
• Cysteine dropped significantly only in one sake sample, correlating with increased off-odor precursor formation.
• In white wine, tryptophan decreased and kynurenine increased under light exposure, consistent with activation of the kynurenine pathway.

Benefits and Practical Applications

These metabolite markers offer objective indicators of beverage quality and oxidative damage. They can guide storage protocols, packaging design, and quality control procedures to maintain flavor and nutritional value during distribution.

Future Trends and Applications

Emerging mass-spectrometry workflows will expand targeted panels and integrate real-time monitoring. Coupled with machine learning, comprehensive metabolite data can predict shelf life, detect adulteration, and optimize supply-chain parameters.

Conclusion

LC/MS/MS–based food metabolomics provides a powerful approach to characterize deterioration in alcoholic beverages. Light-induced oxidation notably alters specific metabolites, underscoring the need for controlled storage conditions.

Instrumentation Used

Shimadzu Nexera X2 UHPLC
Shimadzu LCMS-8060 mass spectrometer