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IMPLEMENTATION-READY CANNABIS TESTING Flowers, Extracts, Edibles, and Concentrates

Brochures and specifications | 2018 | PerkinElmerInstrumentation
GC/MSD, HeadSpace, GC/SQ, HPLC, LC/MS, LC/MS/MS, LC/QQQ
Industries
Food & Agriculture
Manufacturer
PerkinElmer

Summary

Significance of the Topic


Cannabis testing has become a critical pillar in ensuring consumer safety, regulatory compliance, and quality control across an expanding industry. With a complex plant matrix and evolving legislative requirements, laboratories must deploy sensitive and robust analytical methods to detect pesticides, mycotoxins, residual solvents, terpenes, cannabinoids, and heavy metals at trace levels. High-throughput, turnkey solutions enable reliable decision making, reduce downtime, and support consistent strain quality and product safety.

Objectives and Overview of the Study


This whitepaper presents a comprehensive, single-source laboratory workflow for full-spectrum cannabis analysis from raw material to finished product. Key objectives include:
  • Developing unified sample preparation protocols to cover multiple analyte classes
  • Leveraging advanced chromatography and mass spectrometry to meet or exceed state action limits
  • Streamlining laboratory operations with integrated instrumentation and software
  • Maximizing throughput and data integrity while minimizing maintenance and instrument footprints

Methodology and Used Instrumentation


This approach combines complementary analytical platforms tailored to each target compound class. Major techniques include:
  • UHPLC-MS/MS with QSight triple quadrupole (APCI/ESI dual source) for simultaneous pesticide and mycotoxin quantification
  • Headspace GC/MS using Clarus SQ 8 and TurboMatrix sampler for residual solvents and terpene profiling
  • Reversed-phase HPLC with PDA detection (Flexar HPLC) for precise quantitation of twelve primary cannabinoids
  • FT-NIR spectroscopy (Spectrum Two N) for rapid, non-destructive potency screening of THCA/CDBA ratios
  • ICP-MS (NexION 2000) coupled with microwave digestion (Titan MPS) for trace heavy metal analysis

Main Results and Discussion


Methods achieved limits of quantification well below stringent regulatory thresholds (0.005–0.25 µg/g for pesticides/mycotoxins; sub-ppb metals). Single-instrument workflows eliminated the need for separate GC/MS runs for chlorinated pesticides and enabled headspace GC/MS to address both solvents and terpenes. StayClean™ technology and dual-source flexibility reduced matrix-induced maintenance and improved throughput. FT-NIR delivered rapid potency estimates with minimal sample prep, complementing full HPLC assays. ICP-MS detection limits were an order of magnitude lower than California requirements for arsenic, cadmium, mercury, and lead.

Benefits and Practical Applications


These integrated methods offer laboratories:
  • Consistent, reproducible results across multiple analyte classes
  • Reduced capital and operational costs via shared instrumentation
  • Streamlined sample prep and maintenance with high uptime features
  • Regulatory compliance support through validated standard operating procedures
  • Scalability from start-up facilities to high-volume contract labs

Future Trends and Opportunities


The next wave of cannabis analytics will focus on automation, AI-driven data interpretation, and miniaturized sensors for in-field monitoring. Expanded metabolomic profiling, high-resolution mass spectrometry, and multiplexed assays will enable deeper insights into minor cannabinoids and trace contaminants. Integration with laboratory execution systems and cloud-based reporting will drive further efficiency gains and real-time decision support.

Conclusion


A unified, turnkey laboratory solution combining targeted chromatography, mass spectrometry, spectroscopy, and robust software creates a future-proof platform for comprehensive cannabis testing. By meeting stringent sensitivity, selectivity, and throughput demands, laboratories can ensure product safety, regulatory compliance, and rapid business growth.

References


No external references provided in the source text.

Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.

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