Fragmentation of Cannabinoids by Flow Injection Analysis Tandem Mass Spectrometry (FIA-MS/MS)

Abstract

Background: The analysis of plant material from Cannabis sativa L. has long been targeted on its main psychologically active metabolite, Δ9-tetrahydrocannabinol (THC). In addition to the diverse plant composition and medicinal interest in several cannabinoids, these compounds may also be related to the different characteristics of samples sold illegally. Currently, it is indisputable that other cannabinoids should also be considered in cannabis assays. Mass spectrometry has been used to identify and characterize substances in the most different scenarios, and knowing the analyte fragmentation profile is essential for characterizing samples of diverse origin.

Objective: In this work, flow injection analysis-tandem mass spectrometry with electrospray ionization (FIA-ESI-MS/MS) in positive and negative modes was used to evaluate the fragmentation profiles of eight cannabinoids commonly found in cannabis samples: THC, tetrahydrocannabinolic acid, Δ8-tetrahydrocannabinol, cannabidiol, cannabidiolic acid, cannabigerol, cannabigerolic acid and cannabinol.

Methods: By exploring the fragmentation data from mass spectrometry, the samples were classified using a chemometric model of partial least squares discriminant analysis (PLS-DA).

Results: When ESI in negative mode is used with adequate collision energies, it is possible to identify differences in the fragmentation of isomers. Based on that, chemometric tools were employed to classify different samples. The PLS-DA applied to FIA-ESI-MS/MS data yielded satisfactory classification.

Conclusion: Thus, the results presented can be applied as a preliminary tool in the analysis of unknown samples, guiding more accurate investigations in terms of chemical composition.

Highlights: This study of the cannabinoid fragmentation pattern by flow injection MS showed that cannabinoids can be distinguished by their fragmentation spectra after negative electrospray ionization. Multivariate data analysis (PLS-DA) allowed classification of different cannabis samples.