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

Importance of the Topic

The quality and flavor profile of wine are determined by a complex mixture of primary metabolites such as amino acids, organic acids and sugars. Widely-targeted metabolomic profiling using LC-MS/MS and GC-MS/MS enables comprehensive evaluation of these components, supporting more objective quality control, authentication and process optimization in the wine industry.

Objectives and Study Overview

This study aimed to differentiate and classify four types of red wine—Pinot Noir and Cabernet Sauvignon from the USA and Australia—by simultaneously profiling primary metabolites. Multivariate statistical analyses (PCA and HCA) were applied to the acquired metabolomic data to identify characteristic compounds associated with grape variety and geographic origin.

Methods and Instrumentation

Sample Preparation:

• Wine aliquots (0.2 mL) spiked with internal standard and filtered (3 kDa MWCO).
• Filtrate split: one part diluted for LC-MS/MS; remainder evaporated, derivatized (methoximation, trimethylsilylation) for GC-MS/MS.

Data Analysis:

• Quantification by MRM (multiple reaction monitoring) mode.
• Peak area ratios normalized to internal standard.
• Multivariate statistics: Principal Component Analysis and Hierarchical Cluster Analysis.

Used Instrumentation

• LC System: Nexera UHPLC with Discovery HS F5 column.
• Mass Spectrometer: LCMS-8060 (MRM, ESI positive/negative).
• GC System: Gas chromatograph with BPX-5 column.
• Mass Spectrometer: GCMS-TQ8040 (MRM).

Main Results and Discussion

The LC-MS/MS analysis detected 97 metabolites, including amino acids, organic acids and nucleic acid derivatives. Proline was notably elevated across all wines, reflecting its non-fermentable nature. Cabernet Sauvignon from the USA showed comparatively low amino acid levels and separated distinctly in the PCA score plot. GC-MS/MS profiling yielded over 300 compounds and supported a similar classification trend, with the USA Cabernet Sauvignon again positioned separately.

HCA further confirmed clear clustering by grape variety and origin. The first principal component was driven primarily by amino acid variation, while organic acids influenced the second component. These observations suggest that targeted monitoring of key metabolite changes can reveal subtle differences in grape composition and fermentation effects.

Benefits and Practical Applications

• Objective wine classification by chemical fingerprinting.
• Enhanced quality control and authenticity verification.
• Insight into the impact of grape variety and regional terroir.
• Potential for optimizing fermentation protocols based on metabolic markers.

Future Trends and Applications

Integration of widely-targeted metabolomics with high-throughput data analytics and machine learning will enable more precise prediction of sensory attributes and accelerated breeding of grape cultivars. Advances in miniaturized MS platforms may allow on-site wine profiling during production and distribution.

Conclusion

The combined LC-MS/MS and GC-MS/MS metabolomic strategy successfully discriminated wine types by profiling hundreds of primary metabolites. Multivariate statistical analysis identified key compounds influencing classification, demonstrating the method’s value for quality assessment and authentication in the wine industry.