Büttenbender Sabrina Laíz, Carvalho Ânderson Ramos, de Souza Barbosa Fábio, Scorsatto Ortiz Rafael, Limberger Renata Pereira, Mendez Andreas S L
Programa de Pós-Graduação em Ciências Farmacêuticas, Universidade Federal do Rio Grande do Sul, Av. Ipiranga 2752, Porto Alegre, RS, Brazil.
Superintendência da Polícia Federal no Rio Grande Sul, Porto Alegre, RS, Brazil.
J AOAC Int. 2022 Apr 27;105(3):915-927. doi: 10.1093/jaoacint/qsab169.
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.
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.
By exploring the fragmentation data from mass spectrometry, the samples were classified using a chemometric model of partial least squares discriminant analysis (PLS-DA).
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.
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.
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.
长期以来，对大麻植物材料的分析主要针对其主要的具有心理活性的代谢物Δ9-四氢大麻酚（THC）。除了植物成分多样以及对几种大麻素具有药用价值外，这些化合物还可能与非法销售样品的不同特征有关。目前，在大麻检测中考虑其他大麻素是无可争议的。质谱已被用于在最不同的场景中识别和表征物质，了解分析物的碎片图谱对于表征不同来源的样品至关重要。
在这项工作中，采用正、负模式的电喷雾电离流动注射分析串联质谱（FIA-ESI-MS/MS）来评估大麻样品中常见的八种大麻素的碎片图谱：THC、四氢大麻酚酸、Δ8-四氢大麻酚、大麻二酚、大麻二酚酸、大麻萜酚、大麻萜酚酸和大麻酚。
通过探索质谱的碎片数据，使用偏最小二乘判别分析（PLS-DA）的化学计量模型对样品进行分类。
当在负模式下使用ESI并具有适当的碰撞能量时，可以识别异构体碎片的差异。基于此，采用化学计量工具对不同样品进行分类。应用于FIA-ESI-MS/MS数据的PLS-DA产生了令人满意的分类结果。
因此，所呈现的结果可作为分析未知样品的初步工具，在化学成分方面指导更准确的调查。
本研究通过流动注射质谱对大麻素碎片模式进行研究，结果表明大麻素在负电喷雾电离后的碎片光谱可用于区分。多变量数据分析（PLS-DA）能够对不同的大麻样品进行分类。
