Discrimination of Different Beer Sorts and Monitoring of the Effect of Aging by Determination of Flavor Constituents Using SPME and a Chemical Sensor

Significance of the Topic

Monitoring and discriminating flavor profiles in beer is critical for quality control, product consistency, and consumer satisfaction. Conventional chromatographic methods, while accurate, are time-consuming and resource-intensive. Integrating Solid Phase MicroExtraction (SPME) with a chemical sensor platform offers a rapid, sensitive, and automated approach for flavor analysis and aging monitoring in beverages.

Objectives and Overview of the Study

This study aimed to demonstrate a combined SPME–chemical sensor–GC-MS workflow for (1) discriminating different beer styles, (2) distinguishing packaging formats for the same beer, and (3) monitoring flavor changes during short-term aging under light exposure. Five German pilsener beers and one brand filled in both glass bottle and aluminum can were evaluated in fresh state and after 3 and 6 days of open-bottle light exposure.

Instrumentation Used

• Gerstel MPS 2 SPME autosampler for headspace fiber sampling
• Gerstel CIS 4 programmed temperature vaporization inlet
• Agilent 6890-5973N GC-MS system
• DB-Wax capillary column (30 m × 0.25 mm, 0.25 µm film)
• Pirouette chemometrics software for multivariate analysis

Methodology

Beer samples (5 mL) were equilibrated in 10 mL headspace vials at 45 °C for 2 min. A 75 µm Carboxene/PDMS SPME fiber performed headspace extraction for 15 min. Thermal desorption occurred in the CIS inlet at 220 °C, split 5:1. The GC oven ramped from 40 °C to 300 °C at 5 °C/min. Mass spectra were recorded from m/z 45 to 300 in scan mode. Fingerprint mass data were formatted for Pirouette, and Principal Component Analysis (PCA) was applied to identify clustering and variables contributing to discrimination.

Main Results and Discussion

PCA of fresh samples showed clear clustering by beer brand, with over 98 % of variance explained by the first three components. Notably, the same beer in bottle and can formed a single cluster, confirming packaging had minimal impact on fresh flavor. Key discriminating ions included m/z 55, 61, 70, 88, 91 and 104, corresponding to esters and alcohols such as ethyl acetate, 1‐butanol 3‐methyl acetate, ethyl caproate, octanoic and decanoic acid ethyl esters, and phenylethyl alcohol. Aging under light exposure induced measurable shifts in the PCA scores after 3 and 6 days, reflecting oxidation or ester hydrolysis. GC‐MS chromatograms illustrated changes in peak intensities of selected flavor compounds, supporting sensor fingerprint findings.

Benefits and Practical Applications of the Method

Combining SPME headspace extraction with a chemical sensor–GC-MS platform delivers:

• Rapid pass/fail screening for routine quality control
• High sensitivity for volatile flavor compounds without solvent use
• Automated sampling and data acquisition for high throughput
• Capability to flag “out-of-spec” samples and seamlessly switch to full GC-MS mode for compound identification

Future Trends and Applications

Emerging developments may include:

• Integration of Fast-GC methods to further reduce analysis time
• Real-time process monitoring in brewing and fermentation
• Expansion to other food and beverage matrices (wine, juices, dairy)
• Coupling with advanced machine learning models for predictive quality assessment
• Miniaturized, field-deployable sensor modules for on-site testing

Conclusion

This work demonstrates that SPME coupled with a chemical sensor and GC-MS, combined with PCA, enables effective discrimination of beer varieties, packaging formats, and light-induced aging effects. The method offers a versatile, high-throughput solution for flavor profiling and quality control in the beverage industry.

References

1. J. Pawliszyn, Solid Phase Microextraction. The Royal Society of Chemistry, Cambridge, 1999.
2. J. Pawliszyn, Solid Phase Microextraction. Theory and Practice, Wiley-VCH, New York, 1997.
3. K. R. Beebe, R. J. Pell, M. B. Seasholtz, Chemometrics. A Practical Guide, Wiley-Interscience, New York, 1998.