Multi-Component Analysis of Five Beers

Significance of the Topic

The comprehensive profiling of beer constituents by combining liquid chromatography–mass spectrometry (LC–MS) with multivariate statistical analysis provides an objective approach to evaluate product quality, differentiate beer types and support product development. By quantifying key metabolites such as amino acids and nucleosides, researchers and manufacturers can gain deeper insight into flavor, nutritional value and functional properties, ultimately improving quality control and guiding formulation strategies.

Objectives and Study Overview

This study aimed to develop a workflow for simultaneous analysis of low-molecular-weight metabolites in five commercially available beers using a triple-quadrupole LC–MS system. Key goals included:

• Quantitative profiling of 58 metabolites including amino acids and nucleosides.
• Application of principal component analysis (PCA) and hierarchical clustering to classify beers based on their metabolic fingerprint.
• Identification of characteristic compounds that distinguish lager, ale, low-malt and non-alcoholic beers.

Methodology and Instrumentation

Sample Preparation:

• Degassed beer (0.2 mL) spiked with an internal standard.
• Ultrafiltration using a molecular-weight cutoff filter.
• Recovery of filtrate and 200-fold dilution prior to analysis.

Chromatographic Conditions:

• Column: reversed-phase (RP) column.
• Mobile phase A: 0.1 % formic acid in water; B: 0.1 % formic acid in acetonitrile.
• Flow rate: 0.25 mL/min with gradient elution.

Mass Spectrometry Conditions:

• Ionization: ESI in positive/negative switching mode.
• Nebulizing gas: 2 L/min; heating gas: 10 L/min; drying gas: 10 L/min.
• Probe voltage: +4 kV/−3 kV; interface temp: 300 °C; DL temp: 250 °C; block heater: 400 °C.
• MRM transitions optimized in a primary metabolite method package.

Used Instrumentation

• Shimadzu LCMS-8060 triple-quadrupole mass spectrometer.
• Shimadzu primary metabolite method package.
• Traverse™ MS software for multivariate analysis.

Main Results and Discussion

MRM chromatograms revealed substantial variation in amino acid and nucleoside profiles among the five beers. PCA score plots achieved complete separation of each beer type, while loading plots highlighted key markers—such as guanosine, proline and phenylalanine—that define individual products. Hierarchical clustering further grouped lager beers together as well as low-malt and non-alcoholic beers, confirming compositional similarity within these categories. This combined approach elucidated how specific metabolites contribute to sensory and functional differences.

Benefits and Practical Applications

• High-throughput simultaneous quantitation of dozens of metabolites in complex matrices.
• Objective classification of beer products to ensure consistency and authenticity.
• Identification of quality markers to guide brewing process optimization.
• Support for regulatory compliance and product labeling through robust analytical data.

Future Trends and Potential Applications

• Expansion to other beverages and food products for comprehensive quality assessment.
• Integration with sensory and consumer data to correlate metabolite profiles with perceived flavor.
• Application of machine-learning models to predict product quality and shelf life.
• Development of real-time online monitoring systems in production lines using LC–MS and advanced analytics.

Conclusion

The integration of high-sensitivity LC–MS profiling with multivariate statistical tools offers a powerful framework for the rapid and objective evaluation of beer quality. By quantifying a broad range of metabolites and applying PCA and clustering analyses, distinct compositional patterns can be revealed, enabling product differentiation, quality control and informed formulation strategies. This methodology is poised for broader adoption in the brewing industry and other food sectors seeking advanced analytical solutions.