Non-Targeted Aroma Profiling by GC-TOFMS to Compare Beer Samples

Significance of the Topic

The sensory quality of beer is governed by its volatile and semi-volatile aroma compounds. Employing a non-targeted profiling approach enables comprehensive detection of both known and unexpected analytes, deepening insight into flavor contributors. Such profiling supports product quality assurance, process optimization, and enhanced consumer appeal within the brewing industry.

Objectives and Study Overview

This study applied headspace SPME coupled with GC-TOFMS to non-targetedly profile volatile aroma compounds in three craft beer styles (pale ale, India pale ale, and double IPA). By converting total ion chromatograms into chemical fingerprints and performing principal component analysis, the work aimed to differentiate beer varieties based on overall volatile patterns and individual compound variations.

Methodology

• Sample Preparation: Eight replicates per beer style; 4 mL aliquots placed in 10 mL vials sealed with septa.
• SPME Conditions: DVB/CAR/PDMS fiber; incubation at 50 °C for 10 min followed by 10 min extraction.
• GC-TOFMS Analysis: Thermal desorption at 250 °C; Rxi-5ms column (30 m × 0.25 mm × 0.25 µm); temperature ramp of 10 °C/min from 40 °C to 250 °C; TOFMS acquisition over m/z 33–510 at 15 spectra/s.
• Data Processing: Deconvolution, library matching (NIST), and relative quantification via ChromaTOF; export of TIC data for PCA performed in XLSTAT.

Instrumentation Used

• LECO Pegasus GC-TOFMS system
• Supelco 50/30 µm DVB/CAR/PDMS SPME fiber
• Restek Rxi-5ms capillary column
• ChromaTOF software and XLSTAT for chemometric analysis

Key Results and Discussion

• Chromatograms revealed hundreds of peaks spanning esters, alkanes, aldehydes, acids, and terpenes, each matched against spectral libraries.
• PCA of TIC fingerprints produced distinct clusters corresponding to each beer style, underscoring unique volatile profiles.
• Humulene emerged as a highly loaded variable in PCA, with varying relative abundances in pale ale, IPA, and double IPA, reflecting differences in hop varieties and hopping regimes.
• Other discriminatory compounds included 2-nonanone (fruity odor), isobutyl isobutanoate (green apple note), and ethyl heptanoate (pineapple/cognac aroma), each showing style‐dependent concentration differences.

Benefits and Practical Applications

Non-targeted aroma profiling offers:

• Comprehensive detection of known and novel volatile constituents.
• Robust quality control through sample fingerprinting and off-flavor detection.
• Linkage of chemical composition to sensory attributes for product optimization.
• Guidance in recipe development to recreate or tailor specific flavor profiles.

Future Trends and Opportunities

• Integration of machine learning and advanced chemometrics for enhanced pattern recognition and predictive modeling.
• Development of high-throughput or online HS-SPME-GC-TOFMS platforms for real-time monitoring.
• Extension of this approach to other fermented beverages and complex food matrices.
• Expansion of spectral libraries and automation of identification workflows to streamline non-targeted analyses.

Conclusion

The combined use of HS-SPME, GC-TOFMS, and PCA provides a powerful non-targeted strategy for characterizing and distinguishing craft beer aromas. By capturing both overall chromatographic fingerprints and individual compound differences, this method delivers actionable insights for quality assurance, sensory correlation, and flavor innovation in brewing.

References

• NIST Mass Spectral Library for compound identification.
• LECO ChromaTOF software for deconvolution and quantification.
• XLSTAT for principal component analysis.