Metabolic fingerprinting of Cannabis sativa L., cannabinoids and terpenoids for chemotaxonomic and drug standardization purposes. Phytochemistry

Division of Pharmacognosy, Section Metabolomics, Institute of Biology, Leiden University, Leiden, The Netherlands.
Phytochemistry (Impact Factor: 2.55). 10/2010; 71(17-18):2058-73. DOI: 10.1016/j.phytochem.2010.10.001
Source: PubMed


Cannabis sativa L. is an important medicinal plant. In order to develop cannabis plant material as a medicinal product quality control and clear chemotaxonomic discrimination between varieties is a necessity. Therefore in this study 11 cannabis varieties were grown under the same environmental conditions. Chemical analysis of cannabis plant material used a gas chromatography flame ionization detection method that was validated for quantitative analysis of cannabis monoterpenoids, sesquiterpenoids, and cannabinoids. Quantitative data was analyzed using principal component analysis to determine which compounds are most important in discriminating cannabis varieties. In total 36 compounds were identified and quantified in the 11 varieties. Using principal component analysis each cannabis variety could be chemically discriminated. This methodology is useful for both chemotaxonomic discrimination of cannabis varieties and quality control of plant material.

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    • "Cannabis sativa L. is an annual, dioecious herb belonging to the family of Cannabaceae and originating from Eastern and Central Asia. It has long been used as a medicinal plant, intoxicant, and ritual drug, source of fiber, food, oil, medicine, and inebriant since prehistoric times [8] [10] [11]. The chemistry of C. sativa has been studied extensively, and more than 525 different chemical substances have been isolated from the plant [6]. "
    Science & Justice 09/2015; DOI:10.1016/j.scijus.2015.09.002 · 1.42 Impact Factor
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    • "Values were not corrected for water content of the samples. In order to confirm peak identification, samples and available standards were further analyzed by gas chromatography–mass spectrometry (GC-MS) as recently described [30] "
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    • "In the past few years, the emerging field of metabolomics has become an important technology in many research areas such as toxicology and clinical chemistry including disease diagnosis and also therapeutic drug monitoring (Madsen et al., 2009; Roux et al., 2011) as well for food quality related to nutrition (Hall et al., 2008), ecology, plant biochemistry or chemotaxonomy (Fischedick et al., 2010). Metabolomics is a novel experimental methodology, representing an 'omics' approach along with genomics, transcriptomics and proteomics (Oksman-Caldentey and Saito, 2005), often used in combination with the other omics approaches for deeper understanding of complex biological processes, especially metabolism (Sawada et al., 2009). "
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    ABSTRACT: Untargeted metabolomics coupled with chemometric analysis was applied to evaluate and discriminate six Romanian sea buckthorn (Hippophae rhamnoides L.) berries and leaves. Total carotenoids and total phenolics were determined quantitatively by UV-Vis spectrometry. The qualitative evaluation and discrimination was obtained using the FTIR fingerprints (by using Fourier Transform Infrared spectroscopy) of raw carotenoid and phenolic extracts. The average concentration of total carotenoids was 54 and 3.9 mg carotenoids/ 100g DW in berries and leaves, respectively. The average concentration of total phenolics was 746 mg GAE/100g DW in berries, approximately 1.8 times lower than total phenolics found in leaves. By PCA (Principal Component Analysis) of fingerprints (900-1800 cm(-1)), the responsible bands for samples discrimination were identified. In case of total carotenoids extract the biomarker bands were: 1745, 1743, 1500 cm(-1) for berries and 1458 cm(-1) and 1735 cm(-1) for leaves, while for total phenolic extract the key bands were 1731, 1033, 1622 cm(-1) for berries and 1047 cm(-1), 1616, 1512 and 1454 cm(-1) for leaves. FTIR spectroscopy proved to be a simple and sensitive analytical technique that can be successfully used in sample discrimination and classification.
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