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A Comprehensive Review on the Techniques for Extraction of Bioactive Compounds from Medicinal Cannabis

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MOLECULES
卷 27, 期 3, 页码 -

出版社

MDPI
DOI: 10.3390/molecules27030604

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Cannabis; medicinal cannabis; cannabinoids; extraction techniques

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This article comprehensively analyzes the current extraction methods used in the medicinal cannabis industry and scientific literature. It discusses the major bioactive compounds in cannabis, factors affecting extraction yields, and proposes future considerations for effective extraction. Limited research on medicinal marijuana is highlighted, and the development of sustainable extraction methods through mathematical prediction models is recommended for future studies.
Cannabis is well-known for its numerous therapeutic activities, as demonstrated in pre-clinical and clinical studies primarily due to its bioactive compounds. The Cannabis industry is rapidly growing; therefore, product development and extraction methods have become crucial aspects of Cannabis research. The evaluation of the current extraction methods implemented in the Cannabis industry and scientific literature to produce consistent, reliable, and potent medicinal Cannabis extracts is prudent. Furthermore, these processes must be subjected to higher levels of scientific stringency, as Cannabis has been increasingly used for various ailments, and the Cannabis industry is receiving acceptance in different countries. We comprehensively analysed the current literature and drew a critical summary of the extraction methods implemented thus far to recover bioactive compounds from medicinal Cannabis. Moreover, this review outlines the major bioactive compounds in Cannabis, discusses critical factors affecting extraction yields, and proposes future considerations for the effective extraction of bioactive compounds from Cannabis. Overall, research on medicinal marijuana is limited, with most reports on the industrial hemp variety of Cannabis or pure isolates. We also propose the development of sustainable Cannabis extraction methods through the implementation of mathematical prediction models in future studies.

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