Aroma and Metabolite Analysis Using GC-MS and LC-MS and Approach to Craft Beer Development

Significance of the Topic

Aroma and metabolite profiling enables objective evaluation of beer quality and flavor during craft beer development. Traditional sensory evaluation is subjective, while GC-MS and LC-MS techniques provide detailed compound-level data for improved product control.

Study Goals and Overview

This study applied comprehensive GC-MS and LC-MS analyses to craft beers brewed with two wild yeast strains and two commercial yeast strains. The objectives were to characterize aroma and metabolite differences, identify key compounds, and optimize brewing parameters for novel beer development.

Methodology and Instrumentation

• Sample Set: Four beer samples fermented with Wild Yeast 1, Wild Yeast 2, London Ale yeast, and American Ale yeast under identical brewing conditions
• Aroma Analysis: Headspace trapping (HS-20 NX) coupled to single-quadrupole GC-MS (GCMS-QP2020 NX) with Smart Aroma Database for scan, SIM, and MRM analysis
• Targeted Metabolomics: Triple-quadrupole GC-MS (GCMS-TQ8040 NX) with Smart Metabolites Database Ver.2; triple-quadrupole LC-MS (Nexera X3 + LCMS-8060 NX) with Method Package for Primary Metabolites Ver.3; integrated via Multi-Omics Analysis Package and multivariate tools (SIMCA 18)
• Non-Targeted Metabolomics: Quadrupole time-of-flight LC-MS (Nexera X3 + LCMS-9050) in DDA negative mode; data processed with Signpost MS and LabSolutions Insight Explore for peak extraction, alignment, PCA, mass formula estimation, and MS/MS fragment attribution

Main Results and Discussion

• Aroma profiling identified 100 compounds; PCA separated wild and commercial yeast samples on PC1
• Wild Yeast 2 exhibited elevated p-vinylguaiacol, styrene, and beta-damascenone, correlating with spicy sensory notes
• Targeted metabolomics detected 235 metabolites by GC-MS and 104 by LC-MS; combined profiling covered 278 unique compounds
• PCA of metabolite data mirrored aroma trends; Wild Yeast 2 showed low isoleucine and valine and high lactitol and maltose
• Volcano plot analysis highlighted strain-specific metabolite differences
• Non-targeted LC-QTOF analysis extracted 4 309 features; PCA grouped samples by yeast type
• Unknown marker Compound A (RT 8.7 min, m/z 193.0503) was putatively identified as ferulic acid via mass accuracy, database search, MS/MS matching, and standard confirmation

Benefits and Practical Applications

Detailed compound profiling allowed identification of aroma precursors and key metabolites influencing beer character. Insights into yeast nutrient consumption guided adjustment of cereal inputs for stable fermentation. Ferulic acid was linked to spicy aroma generation. These results facilitated the development of a balanced Belgian IPA craft beer “Kocho.”

Future Trends and Potential Applications

• Integration of GC-MS, LC-MS, and non-targeted approaches for comprehensive multi-omics studies
• Enhanced data analysis using AI and machine learning for predictive flavor modeling
• Real-time monitoring of fermentation via online MS and database-driven aroma mapping
• Application of workflows to other fermented beverages and food products for quality control and innovation

Conclusion

Combining targeted and non-targeted MS techniques with advanced data analysis provides a robust framework for objective aroma and metabolite profiling in craft beer development. This approach yields actionable insights for process optimization, flavor enhancement, and product differentiation.

References

1. Multi-Omics Analysis Package. Shimadzu Corporation, 2024.
2. GC/MS and LC/MS Analysis of Metabolites in Hepatitis Model Mouse Serum. Shimadzu Application News, 2022.
3. Pretreatment Procedure Handbook for Metabolite Analysis. Shimadzu Technical Handbook, 2022.