Agilent LC & LC-MS Cannabis Testing
Presentations | 2017 | Agilent TechnologiesInstrumentation
Accurate quantification of cannabis cannabinoids is essential for product safety, regulatory compliance and quality control in pharmaceutical, nutraceutical and recreational markets. Rapid, reliable analytical methods enable laboratories to meet high sample throughput demands while ensuring consumer protection and consistent product labeling.
This study, conducted by CWC LABS and Agilent Technologies, aimed to develop and validate a five-minute UHPLC-DAD method for simultaneous determination of the top ten cannabinoids, including THC, CBD, CBDA, THCA and others. The work focused on optimizing chromatographic conditions, evaluating system robustness and demonstrating routine application to real hemp oil samples.
Sample Preparation and Buffer
Chromatographic System
Quality by Design (QbD) Approach
Software and Detection
The optimized UHPLC-DAD method achieved baseline separation of ten cannabinoids within five minutes, with a resolution of 1.86 between the critical pair. Calibration curves demonstrated sensitivity down to 1–9 ng/mL for compounds like CBG. Carryover was negligible due to an optimized needle-wash protocol. HDR detection prevented peak saturation and enabled quantification of both high- and low-abundance analytes within a single injection.
The method was applied to commercial hemp oil products, revealing variations in CBD content from undetectable to over 50 % above labeled concentrations. This highlights the need for routine potency testing to verify label claims.
Integration of advanced mass spectrometry and AI-driven data processing will further enhance detection limits and throughput. Emerging column chemistries and micro-flow LC systems promise even faster separations with lower solvent consumption. Online coupling with sample preparation platforms and cloud-enabled reporting will streamline workflows and real-time decision making.
The presented UHPLC-DAD method offers a rapid, robust and sensitive solution for cannabinoid potency analysis. Its adaptability, combined with advanced detection and software tools, makes it an effective platform for modern cannabis testing laboratories.
Adams M, Roth A, Stevens J, Kaikaris K, D’Antonio S. Potency Analysis for Cannabinoids using Agilent LC & LC-MS. Agilent Technologies; 2017.
HPLC, LC/MS
IndustriesFood & Agriculture
ManufacturerAgilent Technologies
Summary
Significance of the Topic
Accurate quantification of cannabis cannabinoids is essential for product safety, regulatory compliance and quality control in pharmaceutical, nutraceutical and recreational markets. Rapid, reliable analytical methods enable laboratories to meet high sample throughput demands while ensuring consumer protection and consistent product labeling.
Objectives and Study Overview
This study, conducted by CWC LABS and Agilent Technologies, aimed to develop and validate a five-minute UHPLC-DAD method for simultaneous determination of the top ten cannabinoids, including THC, CBD, CBDA, THCA and others. The work focused on optimizing chromatographic conditions, evaluating system robustness and demonstrating routine application to real hemp oil samples.
Methodology and Instrumentation
Sample Preparation and Buffer
- Automated sample handling using Agilent 7696A Sample Prep Workbench.
- Mobile phase buffer: ammonium formate with formic acid in LC-MS grade water; columns flushed with methanol after use.
Chromatographic System
- Agilent 1290 Infinity II UHPLC with quaternary pump, multisampler (dual-needle), multi-column compartment and diode array detector.
- Zorbax Bonus-RP column (2.1×50 mm, 1.8 µm) or Poroshell (3.0×50 mm, 2.7 µm) at 50 °C.
- Gradient elution at 0.5 mL/min: 72 % MeOH to 95 % over a 5 min run, with a 1 min re-equilibration.
Quality by Design (QbD) Approach
- Evaluated three column lots, three instrument configurations and three operators preparing mobile phase to assess ruggedness.
Software and Detection
- OpenLab 2.1 for data acquisition, method scouting wizard and HDR (High Dynamic Range) functionality.
- Optional mass spectrometry: QTOF and triple quadrupole (QQQ) for enhanced specificity and multi-residue screening.
Main Results and Discussion
The optimized UHPLC-DAD method achieved baseline separation of ten cannabinoids within five minutes, with a resolution of 1.86 between the critical pair. Calibration curves demonstrated sensitivity down to 1–9 ng/mL for compounds like CBG. Carryover was negligible due to an optimized needle-wash protocol. HDR detection prevented peak saturation and enabled quantification of both high- and low-abundance analytes within a single injection.
The method was applied to commercial hemp oil products, revealing variations in CBD content from undetectable to over 50 % above labeled concentrations. This highlights the need for routine potency testing to verify label claims.
Benefits and Practical Applications
- High throughput: five-minute runs support large sample batches in QA/QC laboratories.
- Flexible method development: multi-column selection, method scouting wizard accelerates optimization.
- Comprehensive profiling: simultaneous potency and impurity assessment with optional MS detection.
- Regulatory compliance: accurate quantitation supports labeling requirements and consumer safety.
Future Trends and Opportunities
Integration of advanced mass spectrometry and AI-driven data processing will further enhance detection limits and throughput. Emerging column chemistries and micro-flow LC systems promise even faster separations with lower solvent consumption. Online coupling with sample preparation platforms and cloud-enabled reporting will streamline workflows and real-time decision making.
Conclusion
The presented UHPLC-DAD method offers a rapid, robust and sensitive solution for cannabinoid potency analysis. Its adaptability, combined with advanced detection and software tools, makes it an effective platform for modern cannabis testing laboratories.
Reference
Adams M, Roth A, Stevens J, Kaikaris K, D’Antonio S. Potency Analysis for Cannabinoids using Agilent LC & LC-MS. Agilent Technologies; 2017.
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