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Chemical Profiling and Classification of Cannabis through Electrospray ionization coupled to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry and Chemometrics

Abstract

Cannabis sativa is chemically characterized as terpenophenolic structures, named cannabinoids, which are exclusively found in this plant. In Brazil, the international trafficking of small amounts of cannabis seeds in transport companies has significantly grown in recent years. In that context, combining the chemical profiling data of cannabis to chemometric techniques provides investigative forces with information towards interrupting such illegal activities. In this paper, 68 samples of cannabis seeds proceeding from seizures promoted by the Brazilian Federal Police were germinated, planted, cultivated in controlled conditions in a greenhouse, and analyzed by positive and negative electrospray ionization coupled to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (ESI(±)-FT-ICR MS and ESI(±)MS/MS) techniques considering different growth periods. The chemical profiling using ESI(+)FT-ICR MS enabled the detection of 123 species as cannabinoid compounds or metabolites, and 8 non-cannabinoids constituents. The multivariate techniques applied to FT-ICR MS data yielded satisfactory results to predict plant growth time by means of combination of the genetic algorithm with the partial least squares regression (GA-PLS). The combined information of the positive and negative modes allowed the construction of the PLS regression model with a prediction error of approximately 1 week to determine the growth time of the plants.

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Publication details

The article was accepted on 05 Jun 2017 and first published on 05 Jun 2017


Article type: Paper
DOI: 10.1039/C7AY01294B
Citation: Anal. Methods, 2017, Accepted Manuscript
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    Chemical Profiling and Classification of Cannabis through Electrospray ionization coupled to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry and Chemometrics

    W. Romão, B. T. Borille, R. S. Ortiz, K. D. C. Mariotti, G. Vanini, L. V. Tose, P. R. Filgueiras, M. C. A. Marcelo, M. F. Ferrão, M. Anzanello and R. Limberger, Anal. Methods, 2017, Accepted Manuscript , DOI: 10.1039/C7AY01294B

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