Workflow for Authenticity Testing of Plant Extract Using Revident LC/Q‑TOF and MassHunter Explorer

Significance of Food Authenticity Testing

Food fraud and mislabeling in complex supply chains pose safety, regulatory and brand risks
High-resolution mass spectrometry coupled with non-targeted profiling offers comprehensive fingerprinting
Reliable workflows are needed to detect adulteration, verify origin and identify unknown compounds

Study Objectives and Overview

This work demonstrates a non-targeted LC/Q-TOF and software workflow for assessing authenticity of lavender essential oil
Goals include differentiation of authentic batches, discrimination of geographical sources, detection of common adulterants and identification of unknown markers
Agilent Revident LC/Q-TOF was paired with MassHunter Explorer for feature extraction, statistical analysis and compound identification

Methodology

Sample preparation involved dilution of authentic oils and common adulterants in methanol with replicates for QC and statistical robustness
Authentic lavender oils from Bulgaria, France and China were compared to adulterants such as ho wood, clove, eucalyptus, rosewood and synthetic blends
Chromatography used an InfinityLab Poroshell 120 StableBond-Aqueous column (2.1×150 mm, 2.7 μm), 30 min gradient at 0.25 mL/min and 40 °C
Mass spectrometry employed electrospray ionization in positive mode, full MS (m/z 70–1100) and Auto MS/MS (CE 40 V)
Data processing in MassHunter Explorer included Find and Align for feature extraction, normalization, PCA, volcano plots, hierarchical clustering and volcano analysis

Used Instrumentation

Agilent 1290 Infinity II LC: multisampler, high-speed pump, multicolumn thermostat
Agilent Revident Q-TOF (G6575AA) controlled by MassHunter Acquisition 12.1
Data analysis: MassHunter Explorer 2.0, MassHunter Qualitative Analysis 12.0
Compound identification aided by NIST MS Search, MassBank, MoNA libraries and SIRIUS CSI:FingerID

Main Results and Discussion

PCA separated two Bulgarian batches (LB1 vs LB2) with >90% variance on PC1/PC2, reflecting natural batch variability in compound abundance
Volcano plots and extracted ion chromatograms (EICs) pinpointed markers present only in authentic oils or enriched in adulterants
Geographical origin discrimination: PCA resolved Bulgarian, French and Chinese subtypes based on unique metabolite patterns
Adulteration sensitivity: blending at 1%, 5% and 20% levels was detected; even 1% synthetic adulteration could be distinguished by PCA shifts
Unknown compound identification: a feature at m/z 147.0443 was reproducibly detected, putatively identified as coumarin via Agilent personal database (99.6% match), NIST spectral match and SIRIUS formula and fragmentation tree analysis

Benefits and Practical Applications

This integrated workflow enables rapid screening of plant extracts for authenticity and adulteration
Non-targeted profiling combined with multivariate statistics offers sensitive detection of low-level fraud
Automated feature extraction and alignment streamline data handling across many samples
Direct access to multiple libraries and AI-driven tools improves confidence in unknown compound identification

Future Trends and Opportunities

Expansion of spectral libraries and community-driven databases will enhance identification coverage
Integration of machine learning for real-time anomaly detection and predictive authentication
Miniaturized and in-line HRMS instruments could enable process monitoring and field screening
Blockchain and digital fingerprinting technologies may further secure supply chain traceability

Conclusion

The Agilent Revident LC/Q-TOF combined with MassHunter Explorer delivers a robust authenticity testing workflow
It discriminates batch, origin and adulterants, and identifies unknown markers with high confidence
This method supports quality control in the food and natural products industries

Reference

• Aprotosoaie A. C.; et al. Essential Oils of Lavandula Genus: a Systematic Review of Their Chemistry. Phytochem. Rev. 2017, 16, 761–799.
• Agilent MassHunter Explorer Overview, Agilent Technologies, Inc., 2023.
• Dührkop K.; et al. SIRIUS4: a Rapid Tool for Turning Tandem Mass Spectra into Metabolite Structure Information. Nat. Methods 2019, 16, 299–302.
• Kim H. W.; et al. NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products. J. Nat. Prod. 2021, 84, 2795–2807.