Food Authenticity Testing with the Agilent 6546 LC/Q-TOF and MassHunter Classifier

Importance of the Topic

The increasing incidence of food adulteration and false labeling poses significant risks to consumer safety and undermines trust in food supply chains. Rapid, reliable, and user-friendly authenticity testing methods are essential for routine quality control in the food industry, enabling manufacturers and regulators to verify premium ingredients and detect fraudulent products efficiently.

Study Objectives and Overview

This study demonstrates an integrated workflow for food authenticity testing using the Agilent 6546 LC/Q-TOF mass spectrometer paired with MassHunter Profinder, Mass Profiler Professional (MPP), and Classifier software. The model application involves three mango varieties (Tommy Atkins, Keitt, Ataulfo) and tests the ability to differentiate pure and adulterated samples at controlled ratios.

Methodology and Instrumentation

Certified or well-characterized mango samples were homogenized and extracted via a QuEChERS EN protocol. Extracts were analyzed on an Agilent 1290 Infinity II UHPLC coupled to a 6546 LC/Q-TOF in positive ion mode. Data processing employed MassHunter Profinder 10.0 for feature extraction, MPP 15.0 for statistical modeling (recursive feature extraction, ANOVA, fold-change filtering, and PLS-DA), and Classifier 1.0 for routine sample classification.

Key Results and Discussion

• Over five days and more than 100 injections, mass accuracy remained below 2 ppm, signal stability exhibited <10 % RSD, and retention time drift stayed under 0.1 min.
• Profinder detected 4 185 molecular features; MPP filtering reduced these to 481 key entities used to build a robust PLS-DA model (R2/Q2 metrics validated).
• Classifier software successfully differentiated pure samples (confidence >0.9) from adulterated mixtures (confidence decreasing with higher admixture levels). PCA plots clearly separated the three mango classes and plotted adulterated samples as outliers relative to 95 % confidence ellipses.
• Method precision over repeat runs (n = 10) yielded confidence value RSDs <5 %, demonstrating long-term model stability without frequent retraining.

Benefits and Practical Applications

• Streamlined workflow allows non-specialist technicians to perform authenticity testing without deep data-analysis expertise.
• Rapid sample turnaround—from QuEChERS extraction to classification results in minutes per sample.
• High reproducibility and mass accuracy support reliable QC over extended periods.
• Flexible model update using MPP automation minimizes human error and accelerates method development.

Future Trends and Opportunities

Advances in software automation within MPP and Classifier promise even faster method deployment and easier incorporation of new authentic reference materials. This approach can be extended to other high-value foods (olive oil, honey, wine) and integrated into routine QA/QC laboratories. Coupling machine-learning classifiers with expanding compound databases will further enhance detection of subtle adulteration and support real-time monitoring in supply chains.

Conclusion

The combined use of Agilent 6546 LC/Q-TOF, MassHunter Profinder, MPP, and Classifier software provides a powerful, user-friendly platform for routine food authenticity testing. The workflow delivers fast, accurate, and reproducible results, enabling broader adoption of high-resolution mass spectrometry in quality control laboratories.

References

• Agilent QuEChERS extraction kits and protocols: https://www.agilent.com/en/products/sample-preparation/sample-preparation-methods/quechers/extraction-kits