Accurate Mass Library for Natural Products Based on Compounds Identified in Hemp CBD Oil

Significance of the Topic

Concentrated hemp CBD oils are chemically complex mixtures whose comprehensive characterization is critical for ensuring product quality, safety, and the discovery of bioactive compounds.

Objectives and Study Overview

• Develop a comprehensive accurate‐mass GC/EI Personal Compound Database and Library (PCDL) for natural products derived from hemp CBD oil.
• Demonstrate target and non‐target screening workflows in one‐dimensional GC using high‐resolution GC/Q‐TOF data.

Instrumentation Used

• Gas chromatograph: Agilent 7890.
• Mass spectrometer: Agilent 7250 GC/Q‐TOF with 70 eV electron ionization.
• Thermal modulator: ZOEX ZX2 for GC×GC separations.
• Columns: 30 m × 0.25 mm × 0.25 µm 5% phenyl DB-5MS UI primary; 2.8 m × 100 µm × 0.1 µm DB-HeavyWAX secondary.
• Inlet: Multi‐mode inlet at 280 °C; transfer line at 280 °C; source at 200 °C; quadrupole at 150 °C.
• Carrier gas: helium at 1 mL/min; oven ramped from 60 °C to 290 °C (GC×GC) or 300 °C (1D GC).
• Data acquisition: 50 Hz (GC×GC) or 5 Hz (1D GC); mass range 40–650 m/z.
• Software: Agilent MassHunter (Qualitative, Quantitative, Unknown Analysis v10.x) and GC Image v2.9r2.

Methodology

Five commercial hemp CBD oil samples were analyzed in both one‐ and two‐dimensional GC configurations. Retention indices were determined using an alkane ladder. Tentative identifications in GC×GC data employed NIST17 and NIST20 for initial library matching. Fragment formula annotation and conversion of measured m/z to theoretical values preceded PCDL curation.

Main Results and Discussion

• The curated PCDL comprises approximately 350 entries, each annotated with retention time, retention index, accurate mass spectra, and fragment formulas.
• GC×GC chromatograms distinctly resolved major compound classes (e.g., sesquiterpenoids, monoterpenoids, alcohols, phenols, ketones), facilitating confident identification.
• Compound class distribution in the library: sesquiterpenoids (~27.6%), alcohols (including terpene alcohols and phenols, ~11.1%), monoterpenoids (~10.3%), carboxylic acid esters (~8.3%), and others.
• Non‐target screening yielded true positives with library match scores mostly above 80, while target screening achieved a larger proportion of matches above 90, reducing false positives and improving confidence.
• Both target and non‐target workflows identified a similar total number of compounds across CBD oil and cannabis samples, with target screening demonstrating slightly higher sensitivity.

Benefits and Practical Applications

Installation of this accurate mass library enables rapid, high‐confidence screening of hemp and cannabis products for quality control, regulatory compliance, and research into bioactive natural constituents.

Future Trends and Potential Applications

• Expansion of the PCDL to additional natural product matrices and improved isomer differentiation.
• Integration of machine learning algorithms for automated, high‐throughput compound identification.
• Broader adoption of GC×GC coupled with high‐resolution MS to tackle increasingly complex sample analyses in pharmaceutical, food, and environmental contexts.

Conclusion

A robust accurate‐mass GC/EI library for hemp CBD oil constituents was developed, enabling efficient target and non‐target screening via one‐dimensional GC/Q‐TOF. The approach delivers high match confidence and supports routine analytical and quality control workflows in natural product research.