Semi-quantitative analysis of cannabinoids in hemp (Cannabis sativa L.) using gas chromatography coupled to mass spectrometry.

Luca De Prato, Matthew Timmins, Omid Ansari, Katinka X Ruthrof, Giles E St J Hardy, John Howieson, Graham O'Hara
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Abstract

Background: Hemp (Cannabis sativa L.) is a producer of cannabinoids. These organic compounds are of increasing interest due to their potential applications in the medicinal field. Advances in analytical methods of identifying and quantifying these molecules are needed.

Method: This study describes a new method of cannabinoid separation from plant material using gas chromatography-mass spectrometry (GC-MS) as the analytical tool to detect low abundance cannabinoids that will likely have implications for future therapeutical treatments. A novel approach was adopted to separate trichomes from plant material to analyse cannabinoids of low abundance not observed in raw plant extract. Required plant sample used for analysis was greatly reduced compared to other methods. Derivatisation method was simplified and deconvolution software was utilised to recognise unknown cannabinoid compounds of low abundance.

Results: The method produces well-separated spectra and allows the detection of major and minor cannabinoids. Ten cannabinoids that had available standards could be identified and quantified and numerous unidentified cannabinoids or pathway intermediates based on GC-MS spectra similarities could be extracted and analysed simultaneously with this method.

Conclusions: This is a rapid novel extraction and analytical method from plant material that can identify major and minor cannabinoids using a simple technique. The method will be of use to future researchers seeking to study the multitude of cannabinoids whose values are currently not understood.

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气相色谱-质谱联用半定量分析大麻中的大麻素。
背景:大麻(Cannabis sativa L.)是大麻素的生产者。这些有机化合物由于其在医学领域的潜在应用而日益引起人们的兴趣。需要在鉴定和定量这些分子的分析方法上取得进展。方法:本研究描述了一种从植物材料中分离大麻素的新方法,使用气相色谱-质谱(GC-MS)作为检测低丰度大麻素的分析工具,这可能对未来的治疗有影响。采用了一种新的方法来分离毛状体从植物材料分析大麻素的低丰度未观察到的原料植物提取物。与其他方法相比,大大减少了分析所需的植物样品。简化衍生化方法,利用反褶积软件识别未知的低丰度大麻素化合物。结果:该方法具有良好的分离光谱,可检测出主要大麻素和次要大麻素。该方法可对已有标准的10种大麻素进行鉴定和定量,并可根据GC-MS谱相似性同时提取和分析许多未确定的大麻素或途径中间体。结论:这是一种从植物材料中快速提取和分析大麻素的新方法,技术简单,可鉴别大麻素的主要成分和次要成分。该方法将用于未来的研究人员寻求研究大量大麻素,其价值目前尚不清楚。
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