Discrimination of Different Unifloral Honeys Using an Untargeted High-Definition Mass Spectrometry Metabolomic Workflow

Significance of the Topic

Honey is a valuable natural product whose quality and price depend on its floral origin. Fraudulent dilution or mislabeling of unifloral honeys undermines consumer trust and poses safety concerns. Advanced analytical techniques are needed to authenticate botanical sources and combat food fraud.

Objectives and Study Overview

This study evaluated an untargeted UPLC-HDMS metabolomic workflow to differentiate rape, heather, buckwheat, and Manuka unifloral honeys from various countries and years. It aimed to discover characteristic chemical markers for each floral type and verify marker selection using a targeted UPLC-MS/MS approach.

Methodology

Authentic unifloral honey samples were diluted in methanol/formic acid, sonicated, centrifuged, and randomized for analysis. Part 1 employed UPLC coupled to HDMS with data-independent HDMS^E acquisition in positive and negative ion modes. Multivariate statistical analyses (PCA, OPLS-DA, S-plot) in Progenesis QI and EZInfo identified features differentiating each honey type. Part 2 used UPLC-MS/MS in MRM mode to confirm one key marker, leptosperin.

Instrumentation Used

• UPLC: ACQUITY UPLC I-Class with FTN autosampler
• High-Definition MS: SYNAPT G2-Si HDMS
• Triple Quadrupole MS: Xevo TQ-S
• Software: MassLynx, Progenesis QI, TargetLynx XS, EZInfo

Key Results and Discussion

PCA of HDMS^E data revealed clear clustering of QC, rape, heather, buckwheat, and Manuka honeys, confirming method reproducibility. OPLS-DA and S-plot comparisons highlighted markers such as methyl syringate, leptosin, and leptosperin with significant fold changes (p < 0.0001). Database searches and in silico fragmentation supported tentative identifications. Targeted MRM assays demonstrated selective detection of leptosperin in Manuka samples only.

Benefits and Practical Applications of the Method

• Comprehensive untargeted profiling enables simultaneous discovery of multiple botanical markers.
• Ion mobility adds separation of isomers and spectral cleanup, improving identification confidence.
• Targeted MS/MS verification ensures robust marker confirmation for routine QC workflows.
• Approach supports regulatory compliance and fraud prevention in honey supply chains.

Future Trends and Potential Applications

• Integration of larger metabolite databases and machine learning models for automated classification.
• Development of portable ion mobility-MS platforms for on-site authenticity testing.
• Expansion of workflows to other high-value food products (olive oil, wine, spices).
• Combining metabolomics with genomic or spectroscopic methods for multimodal authentication.

Conclusion

The untargeted UPLC-HDMS metabolomic workflow effectively discriminates unifloral honeys based on unique chemical signatures. Coupling with targeted MS/MS confirmation provides a reliable, scalable solution for honey authentication and fraud detection.

Reference

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