High-throughput quantitative metabolomics utilizing high-resolution accurate mass measurements

Significance of the Topic

Targeted metabolomics using high-resolution accurate mass LC-MS enables reliable quantitation of metabolites in complex matrices such as milk. Such methods support food authentication, quality control, and consumer protection by rapidly identifying biochemical markers of origin, processing, and composition.

Objectives and Study Overview

This study aimed to develop a rapid, robust LC-MS profiling workflow for targeted analysis of amino acids and organic acids in bovine and plant-based milk. The method was designed for high throughput, precise quantitation, and differentiation of milk types based on fat content and organic status.

Methodology

Sample Preparation:

• Milk samples (bovine and plant-based) were pooled, spiked with isotope-labeled internal standards, and extracted using a modified Folch protocol (chloroform:methanol and water).
• After phase separation, extracts were dried under nitrogen and reconstituted in 5% methanol solution.

LC-MS Acquisition:

• Two full-scan methods covering m/z 70–800 at 120k resolution on an Orbitrap Exploris 240 with Easy-IC calibration.
• ESI(+) mode for amino acids and ESI(–) for organic acids.
• Quantitation performed with a 3 ppm mass tolerance filter in TraceFinder software.

Instrumentation

• Thermo Scientific Vanquish Horizon UHPLC system with Hypersil GOLD C18 column (2.1 x 150 mm, 1.9 µm) at 55°C.
• Orbitrap Exploris 240 mass spectrometer with heated ESI source (spray voltage 3.5 kV positive, 3.0 kV negative).
• TurboVap LV nitrogen evaporator for extract drying.
• Xcalibur and TraceFinder software for data acquisition and processing.

Main Results and Discussion

• Calibration curves for all targets exhibited linearity (R2 > 0.99) with CV ≤ 10% across working ranges.
• Method reproducibility was excellent, with retention time shifts < 0.1 min and mass accuracy within 3 ppm.
• Amino acids such as phenylalanine and 2-hydroxyglutaric acid were higher in plant-based milks and helped distinguish milk origin.
• Hippuric acid and orotic acid were elevated in bovine milk, serving as specific markers for animal origin.
• Gluconic acid emerged as a selective marker for soy-based milk.
• Fat content influenced amino acid profiles: low-fat and reduced-fat samples exhibited higher amino acid levels compared to fat-free and whole-fat milks.
• Organic versus non-organic status showed variations in key metabolites, supporting further authentication capabilities.

Benefits and Practical Applications of the Method

• Enables rapid screening and authentication of milk types for food security and consumer protection.
• Provides a high-throughput platform for routine quality control in dairy and plant-based beverage industries.
• Offers robust quantitation with minimal sample preparation and short run times (5 minutes per sample).

Future Trends and Applications

• Expansion of target panels to include additional metabolites relevant to nutritional profiling and authenticity testing.
• Integration with automation and robotics for further throughput enhancements.
• Application of similar workflows to other food matrices and complex biological samples.
• Potential coupling with machine learning for enhanced pattern recognition and predictive analytics.

Conclusion

The developed high-resolution, targeted LC-MS method delivers rapid, reproducible quantitation of key amino acids and organic acids in various milk types. It effectively distinguishes bovine from plant-based milks, differentiates fat content and organic status, and supports food authentication workflows.

References

No external literature references were provided in the source document.