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qNMR Technique For Cannabinoid Analysis is More Efficient Than HPLC Method, Study Claims

by Jamila La Malfa-Donaldson
Published: Oct 29, 2021   
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Researchers at the University of Modena in Italy have published a study showing that a spectroscopy technique can simultaneously characterize and quantify cannabinoids across cannabis products.

Published in the Journal of Pharmaceutical and Biomedical Analysis, the systematic study has compared the use of separation with non-separation methods for the analysis of cannabinoids across various cannabis chemotypes.


qNMR vs HPLC

Since the establishment of the legal cannabis industry, several analytical methods for determining cannabinoid content have been developed and made available. The most common techniques involve chromatography, such as high-performance liquid chromatography (HPLC), typically coupled with diode-array detection (UV/DAD) and/or mass spectrometry (MS).

But, although HPLC is a reliable tool for cannabinoid analysis, there is an increasing need for more cost-effective and faster methods to meet regulatory demands.

Qualitative nuclear magnetic resonance (qNMR), a non-separation technique, utilizes spectroscopy to determine the structural characterization of chemical compounds. As the technique doesn't require the purchasing of standard reference materials to distinguish between peaks, it has a clear cost advantage over HPLC methods.

Previous work has been published with the use of carbon 13 (13C) qNMR to analyse fiber-type cannabis samples. However, the recent study from the University of Modena is the first reported use of 13C-qNMR for the determination of cannabis chemotype.

The researchers directly compared how 13C-qNMR and HPLC techniques analyze major cannabinoids like CBDA, CBD, CBGA, delta-9 THCA, delta-9 THC, and CBN in cannabis inflorescences.

Total acquisition time was 47 minutes for 13C-qNMR and roughly the same for the HPLC run, which took 30 minutes with another 10 minutes post-run.

The researchers used both analytical methods to analyse 27 cannabis samples of the following cannabis types currently available to Italian consumers and patients:

  • Fiber-type inflorescences: high CBDA and CBD
  • Cannabis light: high CBDA and CBD
  • Medical: high CBDA/CBD or high THCA/THC or medium levels of both
  • Recreational: high delta-9 THCA and delta-9 THC

It is well known that the use of 13C-qNMR can raise some complex challenges due to the its low sensitivity, long carbon relaxation time, and the nuclear Overhauser enhancement (NOE) effect.

To overcome the limitations of 13C-qNMR, the cannabis extractions were concentrated. Some nuclei with a long relaxation time were also omitted from the calculations. The NOE effect was prevented by setting the proton decoupling power to zero.

One advantage the qNMR technique offers over HPLC methods is its reduced solvent consumption. HPLC methods can use high amounts of organic solvents for the mobile phase to separate each target compound. But the use of organic solvents generates hazardous, toxic waste and consumes large amounts of resources. Non-separation techniques, such as qNMR, which do not involve the use of solvents, can offer more eco-friendly and cost-effective solutions for cannabinoid analysis.

After analyzing their results, the researchers found a high degree of agreement between the quantitative data from the HPLC and qNMR. Statistically significant differences were found in reported amounts of CBDA and CBD in some of the cannabis light, fibre-type, medical-type samples. Statistically significant differences were also found for delta-9 THCA and delta-9 THC in cannabis light and drug-type samples.

Both methods were fully validated and were comparable to previously published methods.

The results from the study show both analytical tools were reliable for the determination of cannabis chemotype. But, according to the researchers, non-separation techniques, such as 13C-qNMR, should be considered for their reduced solvent consumption and simultaneous identification and quantification of cannabinoids.

 

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