Extraction and isolation of cannabinoids from marijuana seizures and characterization by H NMR allied to chemometric tools.

Sci Justice

Departamento de Química, Universidade Federal do Espírito Santo (UFES), Avenida Fernando Ferrari, 514, Goiabeiras, Vitória, ES CEP: 29075-910, Brazil. Electronic address:

Published: September 2018

Marijuana, a drug derived from the Cannabis sativa L. plant, is the world's most consumed illicit drug. In this paper, a total of 156 marijuana samples seized in the state of Espírito Santo (ES), Brazil were studied and analysed by proton nuclear magnetic resonance (H NMR) spectroscopy to identify the major cannabinoids present. A crude extract of all samples was purified using high performance liquid chromatography so that these compounds could serve as reference substances. Nine fractions were obtained and analysed by H NMR and gas chromatography-mass spectrometry (GC-MS), with five presented cannabinoids. ∆-THC (Δ-trans-tetrahydrocannabinol), ∆-THCA (∆-tetrahydrocannabinolic acid), ∆-THC (∆-tetrahydrocannabinol), 11-hydroxycannabinol, CBV (cannabivarin), and CBN (cannabinol) were found, and their chemical structures were confirmed by GC-MS. The latter compound was obtained with high purity (≈100%), while the others were obtained as less complex mixtures with purity higher than 75% (except for Δ-THC). Principal component analysis (PCA) was used on the H NMR spectra of the 156 samples, and it was found that the samples were grouped according to the months, differentiating into two groups (from July 2014 to January 2015 and from February 2015 to July 2015), where non-grouping was observed from four macro-regions of the ES state (North, Central, Metropolitan, and South). The chemical profile of the seized samples was correlated to the H NMR spectrum of an isolated CBN sub-fraction, in which the group formed by samples seized in the year 2015 presented lower CBN content in the chemical composition. From the PCA score plot, two groups of samples were confirmed using the partial least squares discriminant analysis and orthogonal projections to latent structures classification methods.

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http://dx.doi.org/10.1016/j.scijus.2018.06.005DOI Listing

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