Analysis of Residual Pesticides and Mycotoxins in Cannabis Using UPLC-MS/MS and GC-MS/MS to Meet California Regulatory Requirements

Significance of the Topic

The rapid expansion of medical and recreational cannabis markets has elevated the need for reliable safety testing to ensure consumer protection. Residual pesticides and mycotoxins pose significant health risks if present above regulated levels. Analytical methods that deliver high sensitivity, reproducibility, and minimal sample handling are essential to meet stringent state regulations, particularly in California where action levels range down to 10 ppb for pesticides and 20 ppb for mycotoxins.

Study Objectives and Overview

This study aimed to develop and validate a streamlined workflow combining UPLC-MS/MS and GC-MS/MS for simultaneous monitoring of 66 pesticides and 5 mycotoxins in cannabis flower. By leveraging a standardized database-driven method generation tool, the authors sought to minimize development time while achieving regulatory compliance, robust quantitation, and high sample throughput.

Methodology and Instrumentation

Samples of ground cannabis flower were spiked at relevant action-level concentrations, extracted with acetonitrile, and subjected to dispersive solid-phase extraction (dSPE) cleanup. The final extracts were analyzed by:

• UPLC-MS/MS with a C18 column and gradient elution
• GC-MS/MS with a 5 % phenyl methyl column under pulsed splitless injection

Automated method setup, including MRM transitions and data processing parameters, was performed using a curated pesticide database to ensure consistency across platforms.

Instrumentation

• Waters ACQUITY UPLC H-Class system
• Xevo TQ-S micro tandem quadrupole mass spectrometer (ESI+/-)
• Xevo TQ-GC tandem quadrupole mass spectrometer (EI+)
• XBridge BEH C18 XP 2.1×150 mm, 2.5 µm column
• Rxi-5MS 20 m×0.18 mm×0.18 µm GC column
• MassLynx MS Software with TargetLynx processing and Quanpedia method database

Main Findings and Discussion

Linearity for all pesticide analytes was demonstrated over 25–500 ppb (R²>0.99) and for mycotoxins over 5–100 ppb (R²>0.99) using matrix-matched calibration curves. Key challenges included co-eluting isomers (e.g., spinosad D vs. spinetoram J), which were resolved chromatographically. dSPE cleanup effectively reduced matrix suppression, yielding recoveries between 80 % and 120 % for most targets. GC-MS/MS provided complementary coverage for thermally stable or poorly ionizing pesticides, with reproducibility maintained over 50 sequential injections (RSD <15 %).

Benefits and Practical Applications

• Single workflow for dual LC and GC analyses simplifies laboratory operations.
• Database-driven method generation cuts setup time and reduces manual errors.
• Sensitivity meets or exceeds California action limits for regulated residues.
• High reproducibility supports large batch testing in QA/QC and regulatory labs.

Future Trends and Opportunities

• Expansion of target lists as regulatory agencies update pesticide and mycotoxin requirements.
• Integration of high-resolution mass spectrometry to detect emerging contaminants.
• Automated data analysis and reporting using machine learning for rapid decision-making.
• Miniaturized and field-deployable systems to support on-site testing.

Conclusion

The presented approach delivers a robust, sensitive, and efficient solution for comprehensive pesticide and mycotoxin monitoring in cannabis. Automated method generation and combined UPLC-MS/MS and GC-MS/MS analysis ensure compliance with California regulations while reducing sample preparation complexity and increasing laboratory throughput.

Reference

1. Legality of cannabis by U.S. jurisdiction, Wikipedia (accessed December 2018).
2. California Department of Pesticide Regulation: Cannabis program overview (accessed December 2018).
3. Bureau of Medical Cannabis Regulation: California cannabis law and regulations (December 2018).
4. Tran K. et al., Determination of the Oregon Pesticide List in Cannabis Using dSPE Cleanup and UPLC-MS/MS, Waters application note 720006373EN (September 2018).