DLLME-Gas Chromatography-QuadrupoleTime-of-Flight Mass Spectrometry for Classification of Botanical Origin of Chinese Honey

Importance of the Topic

Classification of honey by its botanical origin is critical for quality control, consumer protection, and fair market valuation. Traditional analyses based on physicochemical parameters and target compounds may lack comprehensive discrimination power. Advanced fingerprinting of volatile and semi-volatile profiles provides a more holistic approach to authenticate and differentiate honey sources.

Objectives and Overview of the Study

This study aimed to develop and validate a rapid, sensitive, and reliable method combining dispersive liquid–liquid microextraction (DLLME) with gas chromatography–quadrupole time-of-flight mass spectrometry (GC/Q-TOF MS) for discrimination of Chinese honey from four botanical origins: rape flower, acacia, linden, and vitex. Key goals included optimization of extraction, data processing, chemometric modeling, and compound identification.

Methodology and Instrumentation

• Sample set: 24 unprocessed honey samples (six per origin) stored at 4 °C.
• DLLME protocol: 2 g honey diluted in water/acetonitrile; vortex and centrifugation; phase separation by addition of water, NaCl, acetonitrile, and CCl₄; collection of CCl₄ extract.
• GC/Q-TOF MS analysis: Agilent 7200 system with HP-5ms UI column; temperature program from 50 °C to 250 °C; helium carrier; splitless injection; full scan 40–650 m/z.
• Data processing: Deconvolution by Unknowns Analysis tool; export to CEF; alignment, filtering, and statistical analysis in Mass Profiler Professional (MPP); ANOVA (P ≤ 0.05), fold change ≥ 2 for feature selection.
• Chemometric modeling: Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) for classification.

Main Results and Discussion

• Over 300 components detected per sample; 165 features showed significant differences across four origins.
• PCA achieved clear separation of all honey groups in two-dimensional space.
• Both PLS-DA and SVM models delivered 100 % correct classification in N-fold cross-validation.
• Key markers were annotated using NIST14 library and confirmed by accurate mass and elemental formula annotation tools.

Benefits and Practical Applications

The combined DLLME-GC/Q-TOF MS approach offers high sensitivity, broad compound coverage, and rapid sample preparation. It enables reliable botanical origin authentication to support regulatory agencies, beekeepers, and food industry stakeholders in ensuring honey traceability and preventing adulteration.

Future Trends and Opportunities

Further integration with high-throughput automation and expanded databases could enhance marker discovery. Multimodal fingerprinting incorporating metabolomics and isotope ratio analysis may provide even deeper discrimination. Real-time screening and portable MS solutions represent promising directions for field applications.

Conclusion

A robust analytical workflow combining DLLME and GC/Q-TOF MS with advanced chemometrics successfully classified Chinese honey by botanical origin with perfect accuracy. The methodology is both practical and scalable for routine authentication tasks.

Instrumentation Used

• Agilent 7200 GC/Q-TOF MS
• HP-5ms UI column (30 m × 0.25 mm × 0.25 µm)
• Mass Profiler Professional software (version B.13.1.1)

Reference

• Mandal, M. D., & Mandal, S. (2011). Honey: its medicinal property and antibacterial activity. Asian Pacific Journal of Tropical Biomedicine, 1(2), 154–160.