Untargeted Metabolomics of Vinegar Using Quadrupole Time-of-Flight LC-MS

Importance of the Topic

Untargeted metabolomics provides comprehensive profiling of small molecules in foods, critical for understanding flavor, nutritional and bioactive compounds. It enables discovery of unknown constituents and supports quality control, product development, and health‐related research in the food and beverage industry.

Objectives and Study Overview

This study applied an untargeted metabolomics strategy to characterize metabolic differences across fourteen commercial vinegar samples. Using the Shimadzu LCMS-9030 QTOF LC-MS and MS-DIAL software, the research team aimed to identify and compare flavor and functional metabolites without prior targeting.

Methodology and Instrumentation

The analytical workflow comprised:

• Sample preparation: 10‐fold dilution of vinegar samples with ultrapure water and addition of p‐chlorophenylalanine as an internal standard.
• UHPLC: Nexera X3 system with Discovery HS F5 column (150 × 2.1 mm I.D., 3 µm) at 40 °C; gradient elution using 0.1 % formic acid in water and acetonitrile; flow rate 0.25 mL/min; injection volume 2 µL.
• Mass spectrometry: LCMS-9030 QTOF with electrospray ionization in positive and negative modes; Data Dependent Acquisition (DDA); MS range m/z 50–1000; MS/MS m/z 10–1000; high‐precision acquisition with mass error <1 mDa.
• Data analysis: MS-DIAL (ver. 5.1) for peak detection, alignment, principal component analysis (PCA), and compound annotation using retention time, exact mass, and spectral libraries.

Key Results and Discussion

PCA of 5309 positive‐mode and 10 386 negative‐mode features revealed three distinct clusters: black rice vinegars (samples 2–3), balsamic vinegars (samples 7–9), and the remaining vinegars. Loading plots highlighted discriminating metabolites:

• Enriched amino acids (e.g., isoleucine, valine) and dipeptides in black rice vinegars.
• Polyphenols such as hydroxytyrosol in balsamic vinegars and quercetin in apple cider samples.
• Citr ic acid identification confirmed by MS/MS spectral matching with reference library (mass error <1 mDa).

These results demonstrate the method’s capacity to profile complex fermentation‐derived and aging‐related compounds.

Benefits and Practical Applications

The untargeted metabolomics workflow offers:

• Extensive metabolite coverage for product authentication and quality control.
• Detection of bioactive and flavor‐related compounds relevant to nutrition and sensory properties.
• High‐confidence compound identification driven by accurate mass and MS/MS data.

Future Trends and Opportunities

Advancements may include:

• Expansion of spectral libraries and integration with public metabolome databases for automated annotation.
• Application of machine learning to predict functional properties and sensory attributes.
• Development of real‐time or inline LC-MS platforms for continuous monitoring in industrial production.

Conclusion

This study demonstrated that untargeted metabolomics using the Shimadzu LCMS-9030 QTOF LC-MS and MS-DIAL effectively distinguishes vinegar types based on detailed metabolite profiles. The approach’s high mass accuracy and robust data analysis enable precise characterization of flavor and functional compounds, supporting both research and industrial QA/QC applications.

References

1. Mori A, Kato N, Iida T, Hattori T. Untargeted Metabolomics of Vinegar Using Quadrupole Time-of-Flight LC-MS. Shimadzu Application News. First Edition: Jun. 2024;01-00681-EN.