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 exhibits a wide range of pharmacologically active compounds that vary by cultivar and growth conditions. Accurate profiling of cannabinoids and terpenes is essential to ensure product consistency, support therapeutic research, and aid chemotaxonomic classification of cultivars.

Study Objectives and Overview

This study presents a combined workflow for both targeted and non-targeted chemical screening of 18 cannabis cultivars (including hemp) using ultra performance liquid chromatography (UPLC) and atmospheric pressure gas chromatography (APGC) coupled with quadrupole time-of-flight mass spectrometry (QToF-MS). Multivariate analysis (PCA) is applied to differentiate chemical profiles and identify characteristic markers.

Methodology and Instrumentation

Cannabis flower samples (0.1 g, n=5 per cultivar) were homogenized and extracted for cannabinoids with acetonitrile using a Geno Grinder, and for terpenes with ethyl acetate and sonication. Data were acquired in data-independent MS² (MSE) mode to collect accurate precursor and fragment ions in a single run.

Instrumentation

• UPLC: Waters ACQUITY UPLC I-Class with CSH Phenyl-Hexyl and CORTECS C18 columns
• GC: Agilent 7890B with Restek Rxi-5MS column and APGC source
• Mass Spectrometer: Waters Xevo G2-XS QToF
• Software: MassLynx 4.2, Progenesis QI, EZinfo for multivariate analysis

Main Results and Discussion

Targeted analysis of 16 cannabinoids and 23 terpenes achieved baseline separation in UPLC-MS and GC-MS chromatograms. PCA of cannabinoid data clearly separated hemp (high CBD, low Δ9-THC) from drug-type cultivars. Extended PCA in EZinfo revealed distinct cultivar clusters (e.g., Mendo Purps, Acai, HumP). Abundance profiling highlighted variation in key markers such as cannabigerol (CBG) and Δ9-THC.

Multivariate loadings plots identified novel unknown features, including a component at m/z 374.2463 tentatively assigned to an unreported cannabinoid class. Terpene profiling by APGC-MS differentiated cultivars based on monoterpene and sesquiterpene expression, and correlation analysis linked terpene patterns to specific cultivar groups.

Benefits and Practical Applications

This integrated workflow enhances confidence in compound identification through simultaneous accurate mass precursor/fragment collection and custom libraries. It supports quality control, cultivar authentication, and chemotaxonomic studies by providing rapid, comprehensive chemical fingerprints.

Future Trends and Potential Applications

Expanding spectral and retention time databases will facilitate broader screening of minor cannabinoids and terpenes. Integration with genetic data and larger sample sets under controlled growth variables can improve cultivar classification. Advanced multivariate and machine-learning tools may enable predictive modeling of therapeutic profiles.

Conclusion

The combination of UPLC-QToF and APGC-QToF with high-resolution data acquisition and multivariate analysis constitutes a robust platform for targeted and non-targeted screening of cannabis chemistry. This approach effectively discriminates cultivars and uncovers novel chemical markers, supporting research, quality assurance, and regulatory compliance.

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