Water content determination in different varieties of Cannabis flowers
The potency of the cannabinoids depends on the accuracy of the values of water content in the medicinal Cannabis flowers. Since the water in the plant material greatly affects its contamination susceptibility and product consistency, an accurate analysis of water content in medicinal Cannabis flowers is necessary to ensure product quality and safety, as well as compliance with numerous state and local regulations. The aim of this research was to develop a specific, accurate, fast, and reliable method for selective determination of water at wide concentration ranges in different varieties of Cannabis flowers for medicinal use. The intended use of the method was the evaluation of the quality of the starting material for production of Cannabis extract. Considering that the Karl Fischer titration (Ph.Eur. 2.5.12) is the only established specific test for quantification of water in a wide variety of samples at wide concentration ranges, its suitability for medicinal Cannabis flowers was demonstrated by proposing suitable titrant-solvent combination, followed by verification of the accuracy, linearity, and sensitivity of the method. The obtained results for water content in more than 40 samples of different Cannabis varieties were in range from 3.8 to 8.5%. The values indicate only water content and do not include amounts of other volatile compounds, such as terpenes, as well as cannabinoids, which can be incorrectly classified as moisture content when using thermogravimetric methods. The proposed method has suitable selectivity, accuracy, and sensitivity for the intended purpose. The small sample amount, in addition to the simple operation conditions and fast analysis allows the use of the method for ‘in process’ control for Cannabis flowers.
Institutions
- Institute of public health of Republic of Macedonia (MK)
- Ss. Cyril and Methodius University in Skopje (MK)
Publication Details
- Journal
- SHILAP Revista de lepidopterología
- Published
- 2024-12-01
- DOI
- https://doi.org/10.33320/maced.pharm.bull.2023.69.01.006
- Primary Topic
- Advanced Graph Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00