Widely-targeted Metabolomic profiling for wines by LC-MS/MS and GC-MS/MS measurement

Importance of the Topic

Advanced metabolomic profiling offers quantitative insights into compounds that define wine flavor and quality. By identifying key metabolites, producers and researchers can objectively assess wine authenticity, fermentation processes, and varietal characteristics.

Study Objectives and Overview

This work aimed to apply a widely-targeted metabolomic strategy using both LC-MS/MS and GC-MS/MS to characterize and differentiate four red wines (Pinot Noir and Cabernet Sauvignon from the USA and Australia). Multivariate statistical techniques (PCA and HCA) were employed to classify wines according to their metabolite compositions.

Methodology and Instrumentation

The experimental workflow included:

• Sample preparation: addition of internal standards, ultrafiltration (3 kDa cutoff), dilution for LC-MS/MS, and derivatization (methoximation and silylation) for GC-MS/MS.
• LC-MS/MS analysis: Nexera system with a pentafluorophenylpropyl column (Discovery HS F5 150 × 2.1 mm, 3 µm), gradient of 0.1% formic acid in water and acetonitrile, MRM acquisition on an LCMS-8060 in positive/negative modes.
• GC-MS/MS analysis: BPX-5 column (30 m × 0.25 mm, df 0.25 µm), split injection (1 µL, ratio 30:1), oven ramp from 60 °C to 330 °C, MRM detection on a GCMS-TQ8040.
• Data processing: normalization of peak areas to internal standards, followed by PCA and hierarchical clustering using dedicated statistical software.

Main Results and Discussion

• LC-MS/MS quantified 63 primary metabolites including amino acids, organic acids, sugars, and nucleic-acid–related compounds.
• GC-MS/MS detected over 300 derivatized metabolites spanning organic acids, amino acids, sugars, and volatiles.
• PCA score plots distinctly separated the four wines; USA Cabernet Sauvignon exhibited notably lower amino acid levels, with proline remaining high due to its non-fermentable nature.
• Loading plots revealed that amino acids primarily drove the first principal component, while organic acids influenced the second, reflecting varietal and fermentation differences.
• Hierarchical clustering corroborated these groupings, highlighting metabolic signatures unique to grape variety and geographic origin.

Benefits and Practical Applications of the Method

This dual-platform metabolomic approach enables:

• Robust quality control and authenticity verification in winemaking.
• Identification of metabolite markers linked to fermentation dynamics and grape varietal traits.
• Objective comparison of wines from different regions or processing conditions.

Future Trends and Potential Applications

• Integration with machine learning for predictive modeling of wine quality based on metabolite profiles.
• Expansion to secondary metabolites and aroma compounds to deepen sensory correlation.
• High-throughput automation for large-scale authentication databases and real-time monitoring in production lines.

Conclusion

The combined use of LC-MS/MS and GC-MS/MS with multivariate statistics offers a powerful framework for comprehensive wine metabolomics. It effectively distinguishes varietal and regional differences, supports objective flavor profiling, and strengthens quality assurance in oenology.

Reference

Shuichi Kawana, Takero Sakai, Tsuyoshi Nakanishi, Atsushi Ogiwara. Widely-targeted Metabolomic profiling for wines by LC-MS/MS and GC-MS/MS measurement. ASMS 2017 WP-183.