Investigation of Aging in Beer Using a New Gas Chromatography Time-of-Flight Mass Spectrometry Benchtop System

Importance of the Topic

The aging of beer profoundly alters its aroma profile, impacting quality, consumer acceptance, and shelf life. Understanding chemical changes during storage enables brewers and quality control laboratories to optimize production, ensure consistent flavor, and detect potential off-flavors before they reach consumers.

Study Objectives and Overview

This investigation employed a non-targeted analytical approach to track volatile and semi-volatile compounds in beer over an accelerated aging time course. By simulating up to 20 months of storage at 40 °C, the study aimed to:

• Identify key analytes whose levels change with aging
• Establish time-dependent trends across the aroma profile
• Demonstrate the capability of a new bench-top GC-TOFMS system for comprehensive discovery analyses

Methodology

Beer samples were subjected to forced aging at 40 °C for intervals equivalent to 0, 1, 2, 3, 4, 5, 6, 8, 12, and 20 months. Each time point was prepared in triplicate and analyzed using headspace solid-phase microextraction (HS-SPME) to concentrate volatiles. Extracted compounds were separated on a polar capillary column and detected by time-of-flight mass spectrometry. Non-targeted data processing included general peak finding, library matching, retention index confirmation, and statistical evaluation (t-tests and regression) to highlight analytes correlating with aging.

Used Instrumentation

• Gas chromatograph: Agilent 7890 coupled to LECO Pegasus BT time-of-flight mass spectrometer
• Autosampler: LECO L-PAL3 with HS-SPME configuration
• Column: Restek Stabilwax, 30 m × 0.25 mm × 0.25 µm
• Carrier gas: Helium at 1.0 mL/min constant flow
• Oven program: 35 °C (4 min), ramp 5 °C/min to 180 °C, then 10 °C/min to 220 °C (hold 5 min)
• Mass range: m/z 35–650; acquisition rate: 10 spectra/s

Key Results and Discussion

Over 300 volatile and semi-volatile compounds were detected and quantified across the aging series. Statistical analysis identified 108 components whose relative intensities significantly changed with storage time. These included:

• Esters that generally decreased, contributing to the loss of fruity notes
• Furan derivatives (e.g., furfural, 2-pentyl furan) that increased, associated with sweet, woody, and earthy aromas
• Terpenes with mixed trends, reflecting complex degradation or formation pathways

Heat map visualization highlighted distinct time-course profiles, illustrating compounds that rose steadily, declined, or varied randomly. Regression models confirmed trends beyond random variation.

Benefits and Practical Applications of the Method

This non-targeted GC-TOFMS approach offers several advantages:

• Comprehensive profiling without predefined targets, enabling discovery of unexpected aging markers
• High-throughput analysis with automated HS-SPME sample preparation
• Full mass range acquisition for retrospective data mining and library-based identification

Practically, brewers and quality laboratories can deploy this workflow to monitor flavor stability, optimize packaging, and extend shelf life by tracking critical aging markers.

Future Trends and Opportunities

Advancements may include:

• Integration with chemometric software and machine learning for automated biomarker discovery
• Real-time headspace analysis coupled with direct-injection MS for faster throughput
• Expansion to other beverage matrices and comparative studies across beer styles

Conclusion

The study demonstrated the power of bench-top GC-TOFMS combined with non-targeted analysis to reveal detailed aroma changes during beer aging. Identifying 108 aging-related analytes provides a foundation for targeted monitoring, quality control, and flavor stability research in the brewing industry.

Reference

Marques, A.; ASBC Methods Bulletin A30; American Society of Brewing Chemists: Chicago, IL, June 2014.