Accurate Mass Library for Natural Products Based on Compounds Identified in Hemp Oil Using High-Resolution GC/Q-TOF

Importance of the topic

This study addresses the analytical challenges posed by complex hemp-derived samples, such as concentrated CBD oils, which contain hundreds of coeluting compounds. Accurate identification of cannabinoids, terpenes and other bioactive constituents is essential for quality control, safety testing, and research in pharmaceutical and food industries.

Objectives and Study Overview

The primary aim was to construct a high-resolution, accurate mass database of natural products derived from hemp CBD oil. The library, implemented as a personal compound database and library (PCDL), supports rapid targeted and nontargeted screening on standard one-dimensional GC/Q-TOF platforms.

Methodology and Instrumentation

A comprehensive two-dimensional GC×GC separation was performed to resolve complex matrices before mass spectral acquisition. Samples from multiple hemp CBD oil varieties were analyzed under identical conditions to generate retention indices and spectra for database curation. Data processing included deconvolution, fragment formula annotation and retention index calculation to ensure high confidence in compound entries.

Used Instrumentation

• Agilent 7250 GC/Q-TOF mass spectrometer operating in EI mode at 70 eV
• Agilent 7890B GC system equipped with Zoex ZX2 thermal loop modulator for GC×GC
• Primary column: Agilent J&W DB-5ms Ultra Inert, 30 m×0.25 mm, 0.25 µm
• Secondary column: Agilent J&W DB-HeavyWAX, 2.8 m×100 µm, 0.1 µm
• Software: Agilent MassHunter Qualitative and Quantitative Analysis, GC Image GC×GC

Main Results and Discussion

A PCDL containing ~350 spectra was built, with over 260 entries linked to chemical structures. Two workflows were evaluated:

• Nontarget screening using SureMass deconvolution and Unknowns Analysis yielded comprehensive compound discovery with match scores optimized for retention indices and accurate fragment masses.
• Target screening in Quantitative Analysis enabled simultaneous suspect screening and quantitation, achieving higher match scores (>90) and reduced false positives.

Comparative tests across six CBD oil samples and a cannabis extract demonstrated that both workflows identified a similar number of true positives, with targeted screening providing slightly higher confidence.

Benefits and Practical Applications

• Rapid identification of complex natural product mixtures in quality control laboratories.
• Versatility of workflows allows adaptation to regulatory or research requirements.
• Retention index integration and accurate mass data minimize misidentifications and enhance reproducibility.
• Free availability of the PCDL supports community-driven expansion for other natural matrices.

Future Trends and Potential Applications

Expanding the library to include additional botanical extracts and emerging cannabinoids can further support metabolomic and safety studies. Integration with machine learning algorithms may automate spectral annotation and discovery of novel compounds in complex matrices. Enhanced GC×GC instrumentation and ion mobility coupling could deliver deeper insights into isomeric profiles.

Conclusion

The developed accurate mass PCDL for hemp natural products streamlines both targeted and nontargeted GC/Q-TOF screening on 1D platforms, offering high confidence in compound identification and efficient workflows for quality control and research applications.

References

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3. Flores-Sanchez I. J.; Verpoorte R. Secondary Metabolism in Cannabis. Phytochem. Rev. 2008, 7, 615–639.