Analysis of Wines Using LCMS™-8060NX Triple Quadrupole Mass Spectrometer (Part 2)

Importance of the Topic

Comprehensive analysis of low molecular weight metabolites in wine provides insights into quality, fermentation processes, and authenticity verification. Food metabolomics using LC-MS/MS enables detection of subtle differences in amino acids, organic acids, and other hydrophilic compounds that influence flavor, aroma, and product classification. This approach supports improvement of nonalcoholic beverages and safeguards against adulteration of high-value wines.

Objectives and Study Overview

This study aimed to distinguish nonalcoholic wine from authentic alcoholic wine and to compare premium quality wines with everyday table wines. A mixture of quality and table wines was also assessed to validate the method’s ability to detect blending. Principal Component Analysis (PCA) was applied to the metabolomic data to visualize group differentiation.

Methodology and Instrumentation

Sample Preparation

• Samples: nonalcoholic wine, quality wine, table wine, and a 50:50 quality–table wine mixture
• Filtration through a membrane filter and 100× dilution with ultrapure water containing 2-morpholinoethanesulfonic acid at 1 µmol/L as internal standard

Instrumentation

• LC System: Nexera X3 with reversed-phase column
• Mobile Phase: A) 0.1% formic acid in water, B) 0.1% formic acid in acetonitrile; gradient elution at 0.25 mL/min; injection volume 3 µL
• MS: LCMS-8060NX triple quadrupole with IonFocus unit; ESI in positive/negative modes; MRM acquisition; optimized gas flows and temperatures for enhanced sensitivity and robustness

Main Results and Discussion

LC-MS/MS analysis detected 67 hydrophilic metabolites including amino acids, organic acids, nucleosides, and nucleotides. PCA score plots separated nonalcoholic and authentic wines along PC1, with nonalcoholic samples enriched in arginine, proline, 4-hydroxyproline, and citric acid, while real wines had elevated lactic and succinic acids. PC2 differentiated quality versus table wines, with the mixture positioned intermediate. Lactic to malic acid ratios confirmed active malolactic fermentation in quality wine, whereas nonalcoholic and table wines retained higher malic acid levels, indicating limited malolactic conversion.

Benefits and Practical Applications

This workflow offers a rapid and sensitive means to profile wine metabolites for quality control, process optimization of nonalcoholic beverages, and detection of adulteration. The high-throughput nature and robustness against complex matrices make it suitable for routine analysis in research and industrial laboratories.

Future Trends and Possibilities

Expanding targeted metabolite panels, integrating metabolomics with sensory and genomics data, and applying machine learning for automated classification are expected to enhance the depth and accuracy of wine analysis. Real-time monitoring and miniaturized LC-MS platforms may further facilitate on-site quality assessments.

Conclusion

Comprehensive LC-MS/MS metabolomics effectively discriminates wine types, elucidates fermentation dynamics, and supports authenticity verification. This approach holds significant promise for improving product quality and consumer confidence in the wine and nonalcoholic beverage sectors.

Reference

• Application News C238: Analysis of Wines Using LCMS-8060NX Triple Quadrupole Mass Spectrometer (Part 2), Shimadzu Corporation, Jun. 2021
• Application News C226: Food Metabolomics Analysis of Regional and Grape Variety Differences, Shimadzu
• Shimadzu LC/MS/MS Method Package for Primary Metabolites Ver. 2 Documentation