Poetzsch Sandra N, Poetzsch Michael, Kraemer Thomas, Steuer Andrea E
Department of Forensic Pharmacology and Toxicology, Zurich Institute of Forensic Medicine, University of Zurich, Zurich, Switzerland.
Swiss Drug Testing GmbH, Winterthur, Switzerland.
Drug Test Anal. 2025 Jun 30. doi: 10.1002/dta.3922.
Cannabis sativa L. constituents, such as cannabinoids, terpenes, flavonoids, and other secondary metabolites, determine the plant's (medicinal) effects and properties in a complex interplay, a phenomenon known as the entourage effect. However, environmental influences like cultivation method, soil, light, and climate might also influence the plant's chemical composition-and thus its therapeutic profile. Much like in viticulture, the concept of a "cannabis terroir" might play an important role in determining the plant's chemical phenotype. The aim of this study was therefore to make these complex properties analytically accessible and develop a comprehensive metabolomics workflow using gas chromatography-high-resolution mass spectrometry (GC-HRMS) and liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS) in positive and negative ionization mode, applying HILIC and reversed phase chromatography to assess multiple chemical classes. Data processing and statistical analysis were done in MS-DIAL and MetaboAnalyst, respectively. The method was applied to 35 CBD-type cannabis flowers grown under different environmental conditions, and compounds belonging to various chemical classes were successfully detected. Principal component analysis revealed distinct clustering of the samples, and key discriminative features were identified, including cannabinoids, terpenes such as β-caryophyllene and α-humulene, cuticular alkanes (e.g., pentacosane and nonacosane), and polar compounds such as choline and trigonelline. The markers enabled a discrimination of samples not only by chemical phenotype but also by cultivation environment, supporting the emerging concept of a cannabis terroir. In conclusion, this study introduces an analytical framework for the comprehensive chemical profiling of cannabis employing GC-HRMS and LC-HRMS analysis and advanced statistical techniques.
大麻（Cannabis sativa L.）的成分，如大麻素、萜类化合物、黄酮类化合物和其他次生代谢产物，在复杂的相互作用中决定了该植物的（药用）效果和特性，这一现象被称为“整体效应”。然而，诸如种植方法、土壤、光照和气候等环境因素也可能影响植物的化学成分，进而影响其治疗特性。与葡萄栽培非常相似，“大麻风土”的概念可能在决定植物的化学表型方面发挥重要作用。因此，本研究的目的是通过气相色谱-高分辨率质谱（GC-HRMS）和液相色谱-高分辨率串联质谱（LC-HRMS/MS）在正离子和负离子模式下，应用亲水相互作用色谱和反相色谱来评估多种化学类别，从而使这些复杂特性能够通过分析获得，并开发出一种全面的代谢组学工作流程。数据处理和统计分析分别在MS-DIAL和MetaboAnalyst中进行。该方法应用于在不同环境条件下种植的35种CBD型大麻花，成功检测到了属于各种化学类别的化合物。主成分分析揭示了样品的明显聚类，并确定了关键的判别特征，包括大麻素、萜类化合物如β-石竹烯和α-葎草烯、表皮烷烃（如二十五烷和二十九烷）以及极性化合物如胆碱和胡芦巴碱。这些标志物不仅能够根据化学表型区分样品，还能根据种植环境进行区分，支持了新兴的“大麻风土”概念。总之，本研究引入了一个分析框架，用于通过GC-HRMS和LC-HRMS分析以及先进的统计技术对大麻进行全面的化学分析。
