Determining Wine Authenticity: A Metabolomics Analysis Using UHPLC ESI/Q-TOF MS

Significance of Topic

Authenticating wine origin is essential to protect consumers and producers from mislabeling and fraud.
Comprehensive metabolomic profiling enables detailed chemical fingerprinting that reflects geographical and production variables.

Objectives and Study Overview

This study aimed to apply ultra-high performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-Q-TOF MS) combined with multivariate statistical analysis to differentiate wines from four French Chateaux and Chinese brands.
The goal included discovery of characteristic metabolic markers, construction of classification models, and testing an unknown sample for authentic origin.

Methodology and Instrumentation

Filtered wine samples were analyzed by a UHPLC gradient with reversed-phase C18 column, followed by full-scan ESI-Q-TOF MS in both positive and negative modes.
Data extraction was performed using a molecular feature extraction algorithm, and chemometric processing was carried out in Mass Profiler Professional software.
Key data filters included occurrence frequency thresholds (>25%, >60%), coefficient of variation (<50%), ANOVA (p<0.01), and fold-change (>2).
PCA and PLS-DA were used for unsupervised and supervised classification, respectively, and targeted MS/MS acquired marker fragmentation spectra.

Instrumentation Used

• Agilent 1290 Infinity UHPLC system with binary pump, autosampler, and thermostatted column compartment
• Agilent ZORBAX Eclipse Plus C18 column (2.1×100 mm, 1.8 μm) at 30 °C
• Mobile phase: 0.1% formic acid/5 mM ammonium acetate in water (A) and methanol/water 95:5 (B); flow 0.4 mL/min, injection 2 μL
• Agilent 6530 Q-TOF mass spectrometer with Dual Jet Stream ESI, drying gas 325 °C, flow 11 L/min, sheath gas 350 °C, capillary voltage 3.5 kV

Main Results and Discussion

Instrument repeatability showed retention time RSD <0.5% and mass accuracy within 5 ppm.
Data alignment yielded over 32,000 positive and 43,000 negative ion features; filtering reduced positive ions to 55 significant markers.
Extracted ion chromatograms revealed group-specific signal patterns, supporting marker validity.
PCA clearly segregated four French Chateaux; inclusion of Chinese samples discriminated geographic origin and flagged an unknown sample clustering with Chinese wines.
PLS-DA model achieved 100% accuracy in cross-validation with average prediction confidence of 78%.
Targeted MS/MS and molecular structure correlation suggested possible compound identities for key markers with high compatibility scores.

Benefits and Practical Applications

This approach enhances wine quality control by providing objective chemical signatures for origin verification and fraud detection.
Non-targeted metabolomics facilitates discovery of novel biomarkers and supports regulatory and commercial authentication workflows.

Future Trends and Opportunities

Expanding the reference database with diverse vintages and regions will improve model robustness.
Integrating additional omics data and automated data processing could accelerate authentication protocols.
Developments in portable mass spectrometry and real-time monitoring may enable on-site testing in vineyards and customs checkpoints.

Conclusion

The combination of UHPLC-ESI-Q-TOF MS and advanced chemometrics proved effective for discriminating wine origins with high accuracy.
Identified metabolic markers and predictive models offer a reliable framework for wine authenticity assessment, with potential for further refinement through broader sample sets and targeted validation.

Reference

• 1. Vaclavik L, Lacina O, Hajslova J, Zeigenbaum J. Anal. Chim. Acta. 2011;685:45.
• 2. Cuadros-Inostroza A, Giavalisco P, Hummel J, et al. Anal. Chem. 2010;82:3573–3580.
• 3. Mandel F. ASMS 2011 Poster WP-222.