Strategies for Targeted and Non-Targeted Screening and Differentiation of Cannabis Cultivars Using UPLC and APGC with Quadrupole Time of Flight Mass Spectrometry

Importance of the Topic

Cannabis products are increasingly used for medicinal and adult-use purposes worldwide. Reliable chemical profiling of cannabinoids and terpenes is critical to ensure product safety, quality, and to correlate therapeutic effects with specific chemical signatures. Comprehensive analytical strategies support regulatory compliance and enable differentiation among diverse cannabis cultivars.

Objectives and Study Overview

This study demonstrates a combined workflow for targeted and non-targeted screening of 18 cannabis cultivars (including hemp) using ultra-performance liquid chromatography (UPLC) and atmospheric pressure gas chromatography (APGC) coupled to high-resolution quadrupole time-of-flight mass spectrometry (QToF-MS). In-house libraries and chemometric tools were used to identify and differentiate cannabinoids and terpenes across multiple samples.

Methodology

• Sample Preparation for Cannabinoids:

• Flowers from each cultivar were homogenized; 0.1 g extracted in acetonitrile, centrifuged and diluted.
• Sixteen authentic cannabinoid standards were combined for targeted analysis.

• Sample Preparation for Terpenes:

• 0.1 g plant material extracted in ethyl acetate, sonicated, centrifuged and analyzed by APGC-MS.
• Twenty-three terpene standards guided method development.

Instrumentation Used

• UPLC-MS: Waters ACQUITY UPLC I-Class with ACQUITY CSH Phenyl-Hexyl or CORTECS C18 columns; Xevo G2-XS QToF in MS^E mode, ESI positive ionization.
• GC-MS: Agilent 7890B GC with Restek Rxi-5MS column; APGC source on Xevo G2-XS QToF, positive ion protonation.
• Software: MassLynx 4.2 for acquisition; Progenesis QI for feature detection, library matching, abundance profiling; EZinfo for multivariate analysis.

Main Results and Discussion

• Chromatographic Resolution: Sixteen cannabinoids and twenty-three terpenes were baseline-resolved, enabling confident assignment.
• Chemometric Differentiation: PCA clearly separated hemp (high CBD, low Δ9-THC) from drug-type cultivars and further distinguished individual varieties.
• Marker Identification: Key cannabinoids such as Δ9-THC, THCA, CBG and terpenes like β-pinene, β-myrcene emerged as discriminant markers. An unknown feature (m/z 374.2463, C₂₃H₃₄O₄) was identified in certain cultivars via database matching.
• Abundance Profiling: Relative signal intensity plots revealed fold-changes up to 409× for novel markers and up to 6× for CBG across samples.

Benefits and Practical Applications

• Rapid Cultivar Classification: The workflow supports high-throughput screening for quality control and strain verification.
• Enhanced Identification Confidence: Simultaneous acquisition of accurate precursor and fragment masses reduces false positives.
• Method Transferability: Standardized chromatographic methods and custom libraries facilitate reproducible analyses in QA/QC and research laboratories.

Future Trends and Possibilities

• Expanded Libraries: Inclusion of additional cannabinoids, terpenes, and metabolites from in-silico and experimental sources.
• Integrated Omics: Combining chemical profiles with genomic data for deeper cultivar characterization.
• Machine Learning: Advanced classification algorithms to predict pharmacological properties from chemical signatures.
• In-field Analytics: Portable or miniaturized HRMS platforms for on-site testing.

Conclusion

A combined UPLC-MS and APGC-MS workflow with high-resolution QToF and advanced informatics enables comprehensive targeted and non-targeted screening of cannabis cultivars. Custom spectral libraries and chemometric analysis deliver robust differentiation of varieties, supporting quality assurance, regulatory compliance, and further research into therapeutic correlations.

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