Brewery Process Monitoring with GC-MS

Significance of the Topic

Monitoring chemical changes during beer brewing is essential for optimizing process efficiency, ensuring consistent aroma profiles, and improving product quality. Volatile and semi-volatile compounds generated at each stage of brewing directly influence flavor, making analytical surveillance critical for quality control and process development.

Objectives and Study Overview

The study aimed to track aroma-active compounds at five key points in the brewing process: pre-boil, post-boil, start of fermentation, end of fermentation, and bright beer conditioning. By comparing chemical profiles across these stages, the work sought to map compound dynamics, identify critical reaction pathways, and demonstrate the value of high-resolution GC-MS analysis.

Methodology and Instrumentation

Headspace solid-phase microextraction (HS-SPME) was used to sample volatile and semi-volatile analytes from 5 mL beer samples in 20 mL vials. Extraction conditions were 35 °C for 10 minutes incubation and 20 minutes fiber exposure. Analytes were desorbed in split-less mode into a gas chromatograph coupled to time-of-flight mass spectrometry (GC-TOFMS). Instrumentation details included:

• Autosampler LECO LPAL3 with DVB/CAR/PDMS fiber
• GC Agilent 7890 with Stabilwax column (30 m × 0.25 mm × 0.25 μm)
• Carrier gas helium at 1.4 mL/min
• Oven program 40 °C (3 min) to 250 °C at 10 °C/min
• Mass spectrometer LECO Pegasus BT, 33–500 m/z, 10 spectra/s

Main Results and Discussion

Total ion chromatograms revealed hundreds of compounds, including esters, terpenes, alcohols, aldehydes, and sulfur-containing species. Key observations:

• Malt-derived hexanal peaked pre-boil and declined after boiling.
• Hop-derived β-myrcene increased markedly post-boil then gradually decreased.
• Ethanol and ethyl octanoate rose during fermentation, reflecting yeast metabolism.
• Coeluting analytes such as isobutyl acetate and α-pinene were resolved by deconvolution algorithms, revealing opposing trends hidden in the TIC.
• Reaction networks involving furfural, furfuryl alcohol, ethanol, and furfuryl ethyl ether were mapped, showing yeast-driven conversions.

Benefits and Practical Applications

This methodology provides brewers with detailed chemical fingerprints at each processing stage. It supports rapid detection of off-flavors, optimization of hopping and fermentation protocols, and targeted adjustments to achieve desired sensory profiles. Automated deconvolution enhances compound identification in complex matrices.

Future Trends and Applications

Advances may include real-time or inline monitoring using fast GC-MS, integration with predictive chemometric models, and expanded libraries for aroma compound identification. Coupling with sensory data and machine learning could enable dynamic process control and flavor forecasting.

Conclusion

GC-TOFMS combined with HS-SPME effectively tracked volatile compound dynamics throughout brewing, revealing stage-specific trends and reaction pathways. Deconvolution played a pivotal role in resolving coelutions, enhancing data quality. This analytical approach offers powerful tools for process optimization and flavor management in brewing.