Non-Targeted Analysis of Metabolites in Beverages Using LCMS-9030

Importance of the Topic

Non-targeted metabolomics provides a comprehensive snapshot of all small-molecule constituents in food and beverage samples, moving beyond traditional targeted methods that focus on predefined compounds.
This approach enables discovery of unknown metabolites that contribute to flavor, quality, and nutritional characteristics, supporting objective assessment and innovation in food science and industry.

Objectives and Study Overview

This study aimed to apply non-targeted LC-MS analysis to various beer and beer-like beverages to:

• Identify characteristic metabolite profiles linked to raw materials and processing methods.
• Compare metabolic fingerprints across lager beer, ale beer, low-malt beer, beer-like beverage, and non-alcoholic beers.
• Demonstrate a workflow integrating accurate mass analysis and multivariate software for objective evaluation of beverage samples.

Methodology and Instrumentation

Sample Preparation

• Six beverage types were degassed and diluted tenfold with ultrapure water prior to analysis.

Chromatography and Mass Spectrometry

• The LC method from the LC/MS/MS Method Package for Primary Metabolites was employed using a reverse-phase column at 40 °C, gradient elution with 0.1% formic acid in water and acetonitrile, 0.25 mL/min flow, and 3 µL injection.
• Mass analysis was performed in positive ESI mode with data-dependent acquisition (DDA) capturing precursor and MS/MS spectra; key MS parameters included DL temperature 250 °C, block heater 400 °C, interface 300 °C, and CID gas pressure 230 kPa.

Data Processing and Multivariate Analysis

• Signpost MS software aligned 1 698 detected peaks based on retention time and m/z, followed by Pareto-scaled principal component analysis to extract characteristic features.
• Compound identification relied on an exact mass database for endogenous metabolites and LabSolutions Insight Explore™ for predicting unknowns, validated against reference standards.

Main Results and Discussion

The non-targeted approach detected 1 698 metabolite features across six beverage samples.
Principal component analysis segregated samples according to fermentative and ingredient-based differences:

• Lager beer, ale beer, and one non-alcoholic beer clustered together, sharing amino acids (proline, tyrosine, arginine, tryptophan), guanosine, and a barley-derived alkaloid (hordenine), reflecting common fermentation profiles.
• Low-malt beer and another non-alcoholic beer grouped with elevated ascorbic acid, indicating antioxidant presence.
• The beer-like beverage, formulated with soy protein, exhibited distinct profiles with soy isoflavones (genistein, glycitein) and emulsifiers (tributyl citrate, triethyl citrate), highlighting ingredient-driven metabolomes.

Benefits and Practical Applications

• Non-targeted LC-MS workflows enable comprehensive quality control by detecting unexpected or novel compounds in beverages.
• Objective metabolomic profiling supports authentication, batch-to-batch consistency, and formulation optimization.
• The approach can be extended to monitor nutritional markers and detect adulteration or spoilage in food products.

Future Trends and Potential Applications

• Integration with high-resolution instruments and expanded metabolite databases will enhance identification coverage and confidence.
• Machine learning algorithms could improve pattern recognition and predictive modeling for flavor and quality attributes.
• Non-targeted metabolomics may evolve towards real-time monitoring in production environments and personalized nutrition analysis.

Conclusion

Non-targeted metabolomic analysis using the LCMS-9030 QTOF system and multivariate software effectively discriminates beverage samples based on raw materials and processing. The workflow demonstrates robust potential for objective evaluation of taste, quality, and nutritional value in food science applications.

Used Instrumentation

• UHPLC: Nexera™ X3 with reverse-phase column, 40 °C column oven, gradient elution with 0.1% formic acid modifiers.
• Mass Spectrometer: Shimadzu LCMS-9030 quadrupole time-of-flight with ESI positive mode and data-dependent acquisition.
• Data Analysis Software: Signpost MS (Reifycs Inc.) and LabSolutions Insight Explore™.