Classifcation and Visualization of Beer Quality Using GC-MS and GC-FID

Significance of the Topic

Beer production consistency is challenged by variations in raw materials and processes across breweries.
Sensory evaluation remains the standard for quality control but is inherently subjective.
Metabolomics profiling using GC-MS and GC-FID offers an objective approach to classify beer quality and identify key flavor-contributing compounds.

Objectives and Study Overview

• Study 1: Classification of five different beer brands using GC-MS/MS and GC-FID with PCA comparison.
• Study 2: Classification of the same beer brand brewed in different plants and lots using the same analytical approach.

Methodology

Samples were degassed, spiked with an internal standard (2-isopropylmalic acid), and metabolites were extracted, dried, and derivatized via methoximation and trimethylsilylation.
Data acquisition was performed by GC-MS/MS and GC-FID systems followed by principal component analysis in SIMCA 15 to visualize sample clustering.

Used Instrumentation

• GC-MS/MS: Shimadzu GCMS-TQ8040 triple quadrupole, DB-5 column (30 m × 0.25 mm I.D., 1 µm), MRM with 950 transitions; Smart Metabolites Database (475 compounds).
• GC-FID: Shimadzu GC-2030, SH-Rtx-1 column (60 m × 0.32 mm I.D., 1 µm), FID detector at 330 °C.

Main Results and Discussion

In Study 1, PCA of GC-MS/MS data clearly separated five beer brands, with loading plots identifying sugars (e.g., fructose, glucose), organic acids, and amino acids as key markers.
GC-FID data presented similar clustering patterns, confirming its utility for routine screening, although it lacked the detailed compound annotation of GC-MS/MS.
In Study 2, both GC-MS/MS and GC-FID PCA distinguished beers by brewery plant and lot, revealing consistent metabolic fingerprints tied to production conditions.

Benefits and Practical Applications

• Provides objective metabolomic profiles to complement sensory tests for quality control.
• Enables identification of marker metabolites for targeted flavor adjustment and standardization.
• Offers GC-FID as a low-cost, high-throughput routine screening tool alongside GC-MS/MS for detailed analysis.

Future Trends and Possibilities

Expanding compound libraries and annotation capabilities for GC-FID to bridge the gap with GC-MS/MS.
Integrating multi-platform metabolomics (LC-MS, NMR) and machine learning to build predictive quality models.
Automating on-line monitoring systems in breweries for real-time quality assessment.
Applying this metabolomics approach to other fermented beverages and food products.

Conclusion

Metabolomic profiling using GC-MS/MS and GC-FID combined with PCA offers a robust framework for objective classification and visualization of beer quality across brands and production variables.
This dual-platform approach enhances quality control workflows by balancing detailed compound-level insights with cost-effective routine screening.

Reference

No additional references were provided.