The ACQUITY RDa for Routine Food Profiling – Detecting the Unexpected in Honey

Importance of the Topic

Honey is a high-value natural product widely used in food, pharmaceuticals and consumer goods. Its rich antioxidant and therapeutic properties make it attractive, but high market value also drives fraud through adulteration or illicit fortification. Reliable analytical approaches are critical to protect consumers, maintain supply chain integrity and preserve beekeeping ecosystems.

Study Objectives and Overview

This study evaluated an untargeted metabolomics workflow on a bench-top orthogonal acceleration time-of-flight LC-MS system (ACQUITY RDa Detector) to:

• Differentiation of polyfloral versus monofloral (Manuka) honey samples.
• Detection of undeclared pharmaceutical adulterants in a honey-based health supplement.

The assay integrates simplified instrument setup, automated data acquisition and multivariate statistics for food profiling.

Methodology and Instrumentation

Sample Preparation and Chromatography:

• Extraction of 1 mL honey with methanol/water (30/70) containing 0.1% formic acid.
• Centrifugation, sonication and dilution in UPLC vials; 5 µL injection.
• Separation on ACQUITY UPLC I-Class PLUS with BEH C18 column (2.1 × 100 mm, 1.7 µm) at 45 °C.
• Gradient elution from 1% to 90% acetonitrile over 9 minutes.

Mass Spectrometry and Data Processing:

• ACQUITY RDa Detector in positive pseudo-MSE mode (50–2000 m/z, 10 Hz scan).
• Automated setup and calibration via SmartMS Technology and UNIFI Software.
• Untargeted peak picking in UNIFI, marker generation (~40,000 markers), multivariate analysis (PCA, S-Plot) in EZinfo.

Main Results and Discussion

PCA score plots showed clear clustering of polyfloral, monofloral and QC samples with low technical variance. The health supplement honey separated distinctly. An S-plot comparison against QC identified a unique marker at m/z 390.1456, tentatively assigned to tadalafil via Chemspider search of precursor and fragment ions. Quantitative calibration in UNIFI yielded R2 = 0.994 over 500–3000 ng/mL, estimating supplement tadalafil levels above pharmaceutical dosage limits and indicating potential consumer risk.

Benefits and Practical Applications

This workflow delivers:

• Comprehensive, unbiased metabolic profiling in complex food matrices.
• Streamlined instrument setup and automated data processing for new users.
• Rapid detection of botanical origin, contaminants and illicit adulterants.

It supports quality control, fraud prevention and regulatory compliance in food and supplement industries.

Future Trends and Opportunities

Advancements may include integration of artificial intelligence for marker discovery, expansion to other food and beverage matrices, real-time monitoring in production lines and cloud-based data sharing for global food authenticity networks.

Conclusion

The ACQUITY UPLC I-Class PLUS combined with the ACQUITY RDa Detector and UNIFI workflow enables efficient untargeted metabolomic profiling. It differentiates honey varieties and uncovers hidden adulterants with high confidence, offering a robust platform for food integrity testing.

Reference

1. Grand View Research. Honey Market Size, Share & Trends Analysis Report 2019–2025.
2. Poelsma, J. Honey Fraud – The Impact on Beekeeping, 2020.
3. Wallace et al. Discrimination of Unifloral Honeys using Untargeted HDMS Metabolomic Workflow. Waters Application Note 720005963EN, 2017.