Investigation into Quality Evaluation Methods Involving Total Analysis of Metabolites in Beer

Importance of the topic

Assessing the quality and consistency of beer products is essential for breweries and quality control laboratories. Comprehensive profiling of small‐molecule metabolites combined with multivariate statistical analysis provides an objective approach to distinguish between beer brands, production sites and individual lots. This analytical strategy supports product standardization, process optimization and rapid detection of quality deviations.

Objectives and Study Overview

This study aimed to perform a total metabolite analysis of multiple beer samples to determine whether distinct metabolomic fingerprints can:

• Differentiate between beer brands
• Reveal quality variations among production factories
• Identify lot‐to‐lot differences within the same factory

The work consisted of two primary investigations: Analysis 1 compared brand‐level differences, and Analysis 2 examined factory and lot‐level variations within select brands.

Methodology and Used Instrumentation

Samples from lager beers A, pale ales A, B and C, and an IPA (all obtained from various factories and lots) underwent the following workflow:

1. Addition of 2‐isopropylmalic acid internal standard to each beer aliquot
2. Deproteinization and extraction of hydrophilic metabolites
3. Drying in a centrifugal concentrator
4. Derivatization with methoxyamine hydrochloride in pyridine, followed by MSTFA treatment
5. Analysis by triple quadrupole GC-MS in MRM mode

Used Instrumentation:

• GC-MS System: Shimadzu GCMS-TQ8040 triple quadrupole gas chromatograph mass spectrometer
• Column: DB-5, 30 m × 0.25 mm I.D., 1.00 µm film thickness
• Carrier Gas: Helium (linear velocity 39.0 cm/s)
• Injection: Splitless, purge after 1 min, 5 mL/min purge flow
• Oven Program: 100 °C hold 4 min, ramp 10 °C/min to 320 °C, hold 11 min
• Ion Source Temperature: 200 °C; Interface: 280 °C; EI ionization; MRM acquisition; loop time 0.25 s
• Data Analysis Software: Smart Metabolites Database™ (compound identification) and SIMCA® 15 (multivariate analysis)

Main Results and Discussion

Analysis 1 (Brand Differences):
PCA score plots showed clear separation among the five beer brands. Loading plots and quantitative data pinpointed key metabolites responsible for differentiation. For example:

• Lager beer A was enriched in organic acids (phenylpyruvic, glutaric), sugars (xylose, arabinose) and amino‐acid derivatives
• Pale ale A contained elevated levels of maltitol, cadaverine, 4‐aminobutyric acid and aromatic amino acids
• Pale ale B and C and the IPA exhibited distinct saccharide profiles (e.g., galactose, mannose in pale ale C; fructose, sorbose, sebacic acid in IPA)

Analysis 2 (Factory/Lot Differences):
PCA and hierarchical clustering of lager beer A revealed clear factory‐specific metabolomic patterns. Pale ale A showed separation among factories and even individual lots based on saccharide and amino‐acid metabolites, while pale ale B exhibited minimal lot‐to‐lot variation. Identified markers included trehalose, glyceric acid and 3‐phenyllactic acid for factory A versus allose, tyramine and 2′‐deoxyuridine in factory B.

Benefits and Practical Applications

This combined GC-MS and multivariate approach offers:

• Objective, quantitative indicators of beer quality and authenticity
• Tools for monitoring consistency across production sites and batches
• Rapid screening for process deviations or raw‐material variations

Future Trends and Applications

Advances may include integration with real‐time data analytics, expansion of metabolite libraries, application to additional beverage matrices, and the use of machine learning models for predictive quality control. On‐line or at‐line implementations in breweries could enable dynamic process adjustments based on metabolic signatures.

Conclusion

The study demonstrates that total metabolite profiling by GC-MS in conjunction with multivariate statistics can successfully distinguish beer brands, production factories and individual lots. Key metabolite markers identified here may serve as objective quality metrics and support improved process control in the brewing industry.

Reference

Application News No. M280, Shimadzu Corporation, August 2019.