Phytochemical Characterization of Cannabis sativa L. Roots from Northeastern Brazil.
Jackson Roberto Guedes da Silva AlmeidaDaniel Amando NeryKatia Simoni Bezerra LimaMaria Eduarda Gomes da Cruz SilvaTarcísio Cícero de Lima AraújoNathália Andrezza Carvalho de SouzaRodolfo H V NishimuraCamila de Souza AraújoAna Paula de OliveiraJackson Roberto Guedes da Silva AlmeidaLarissa Araújo RolimPublished in: Chemistry & biodiversity (2023)
This article describes the phytochemical study of Cannabis sativa roots from northeastern Brazil. The dried plant material was pulverized and subjected to exhaustive maceration with ethanol at room temperature, obtaining the crude ethanolic extract (Cs-EEBR). The volatile compounds were analyzed by gas chromatography coupled with mass spectrometry (GC/MS), which allowed to identify 22 compounds by comparing the linear retention index (LRI), the similarity index (SI) and the fragmentation pattern of the constituents with the literature. By this technique the major compounds identified were: friedelan-3-one and β-sitosterol. In addition, two fractions were obtained from Cs-EEBR by classical column chromatography and preparative thin layer chromatography. These fractions were analyzed by NMR and IR and together with the mass spectrometry data allowed to identify the compounds: epifriedelanol, friedelan-3-one, β-sitosterol and stigmasterol. The study contributed to the phytochemical knowledge of Cannabis sativa, specifically the roots, as there are few reports on the chemical constituents of this part of the plant.
Keyphrases
- mass spectrometry
- gas chromatography
- liquid chromatography
- tandem mass spectrometry
- room temperature
- high resolution mass spectrometry
- high performance liquid chromatography
- high resolution
- capillary electrophoresis
- gas chromatography mass spectrometry
- healthcare
- systematic review
- emergency department
- magnetic resonance
- machine learning
- oxidative stress
- simultaneous determination
- deep learning
- artificial intelligence
- data analysis
- neural network