Identification and quantification of molds in cannabis inflorescences and prevention of their development during cultivation and storage
Research team: Dr. Yaakov Shimshoni (PI). Dr. Davide Kengisbuch (Co-PI) and Dr. Tarin Paz-Kagan (CI)
The cannabis plant is increasingly recognized as a treatment for a variety of medical conditions, including cancer, inflammatory bowel disease, pain, and neurological disorders. Its economic and agricultural significance is also growing. In Israel, despite having dozens of farms cultivating medical cannabis, there is only partial fulfillment of the demand for approximately 112,000 patients. This shortfall is partly because some farms fail to produce high-quality inflorescences that meet the Ministry of Health's guidelines, leading to necessary imports from other countries. A primary issue in the cultivation of cannabis in Israel is disease development, particularly from Botrytis and Alternaria, during growth and storage. Our previous research indicated that these molds significantly reduce cannabinoid concentrations in the inflorescences, diminishing their medical efficacy. Moreover, due to patient health risks, mold contamination disqualifies the product from the market. Challenges in preventing mold development and early detection during growth and storage are yet to be fully addressed and demand comprehensive research for practical solutions. Our preliminary experiments showed that hexanoic acid treatment effectively prevented Botrytis development and increased cannabinoid concentration. Hexanoic acid, a primary plant metabolite, stimulates the plant's defense system and aids secondary metabolite production, including cannabinoids. In this research program, we aim to confirm the effectiveness of hexanoic acid treatment against Botrytis and test its activity against Alternaria. This innovative and safe treatment method has significant economic and medical implications for preventing mold growth and increasing active compound concentrations. The current standard for mold contamination assessment in inflorescences involves mold isolation and characterization on Petri dishes, which takes one to two weeks. This method falls short in early detection. Recently, our lab developed a rapid, spectral-based machine learning system for efficient cannabinoid and terpene quantification. This method can identify and quantify Botrytis in under a minute, even at low infection levels not visible to the naked eye, potentially replacing Petri dish seeding. Our ongoing research focuses on refining hexanoic acid treatment for mold prevention during growth and storage, developing the spectroscopic method for Alternaria identification and quantification, and cannabinoid quantification. Integrating these two technologies reduces economic losses and protects patient health from mold exposure.
