Integrated Analysis of Aromatic Components and Metabolites in Beer Samples Using GC-MS Smart Databases

Significance of the Topic

Assessing the aromatic profile and metabolic composition of beer is vital for quality control, sensory evaluation and product development.
Comprehensive profiling of volatile aromas and non-volatile metabolites provides a holistic view of flavor attributes and biochemical markers.

Objectives and Study Overview

This study aimed to perform an integrated analysis of aroma compounds and metabolites in both commercially available beers and experimental brews developed with novel yeast strains.
Smart Aroma and Smart Metabolites Databases were employed to streamline GC-MS and GC-MS/MS workflows, followed by multivariate and multiblock statistical evaluations.

Methodology and Instrumentation

Aroma analysis conditions:

• GC-MS system: GCMS-QP™2020 NX with HS-20 NX headspace sampler
• Column: InertCap Pure-wax, 30 m×0.25 mm I.D., 0.25 µm film
• Oven program: 50 °C hold (5 min), 10 °C/min to 250 °C, 10 min hold
• Acquisition: Scan/SIM, m/z 35–400

Metabolite analysis conditions:

• GC-MS/MS system: GCMS-TQ™8040 NX with AOC-20i+s auto-injector
• Column: DB-5MS, 30 m×0.25 mm I.D., 1.0 µm film
• Derivatization: Methoximation and trimethylsilylation per Application News M280
• Oven program: 100 °C hold (4 min), 10 °C/min to 320 °C, 11 min hold
• Acquisition: MRM, loop time 0.3 s

Key Results and Discussion

Commercial beers:

• 143 aroma compounds and 375 metabolites identified across nine brands.
• PCA separated lager, India Pale Ale (IPA), white ale and barrel-aged styles based on signature volatiles (e.g. linalool, ethyl acetate) and metabolites (e.g. inositol, xylitol).
• MOCA highlighted high correlations such as linalool with inositol in IPA samples, indicating linked flavor and metabolic patterns.

Experimental beers under development:

• 140 aromas and 361 metabolites profiled in brews using New Yeast, Yeast1 and Yeast2.
• PCA revealed distinct clusters for each yeast, with New Yeast showing elevated alpha-humulene and glycine.
• Integrated MOCA confirmed strong aroma-metabolite associations (e.g. alpha-humulene with glycine) in novel yeast beers.

Benefits and Practical Applications

Streamlined workflows using Smart Databases reduce method development time and support high-throughput profiling.
Multivariate and integrated multiblock analyses facilitate objective differentiation of beer styles and yeast strains.
Data serve as robust quality control metrics and guide selection of raw materials and fermentation conditions.

Future Trends and Potential Applications

Expansion of spectral libraries and inclusion of emerging metabolite classes will enhance coverage.
Integration with machine learning models promises predictive flavor mapping and real-time monitoring.
Multimodal approaches combining sensory data with aroma-metabolite networks will drive deeper product insights.

Conclusion

The combined use of Smart Aroma and Metabolites Databases with multivariate and MOCA analyses enabled efficient, comprehensive characterization of beer flavor chemistry.
The workflows demonstrated clear discrimination of beer types and yeast-dependent flavor signatures, offering valuable tools for quality control and product innovation.

References

1. Application News 01-00137: Comprehensive aroma profiling of commercially available beers.
2. Application News M280 (LAAN-A-MS063A): Derivatization procedures for GC-MS metabolite analysis.