Chemotyping: Classifying cannabis strains by chemical composition
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An approach to optimizing the chemotyping of cannabis strains has been outlined by researchers at Ohio University and Chemistry Mapping Inc. Specifically, they evaluated the impact of several mass spectrometry (MS) data pre-processing techniques to identify which strategy helps to provide the most accurate and useful chemical fingerprint of cannabis samples. Their findings were published in Talanta. The research highlights the importance of carefully evaluating and selecting data pre-processing parameters.
A rapid method for characterizing botanicals
From cultivators and breeders wanting to patent specific strains with specific chemical compositions, to clinicians demanding more information on the medical benefits of individual cannabis strains, an accurate view of the chemical profile of any given cannabis strain is of interest for many reasons.
Speaking to us about the story behind the group’s work, corresponding author, Peter Harrington, Professor of Chemistry, Ohio University, explained that the team was first recruited to work on characterizing botanicals using chemical profiling by the United States Department of Agriculture (USDA). Following the publication of many papers on characterizing ginsengs and black cohosh, they were enlisted by Chemistry Mapping Inc. to apply their techniques to cannabis products. Since then, the team have published several papers on cannabis. Dr Harrington highlighted two in particular relating to a high-throughput method of extracting plant material into deuterated chloroform and then characterizing it by nuclear magnetic resonance spectroscopy.
Prof. Harrington told us “The goal is to develop a quick method of measuring the chemical composition of cannabis, so we use spectroscopic methods to analyze extracts, and skip a chromatographic separation step that usually takes longer. Instead of identifying and quantifying each component in the spectrum, the spectrum is treated as a fingerprint. Using chemometrics and machine learning, we then can group the samples into classes based on their observed chemical composition. We refer to this procedure as chemotyping. The goal is to correlate these groups with desired pharmacological properties, so that industry can have some quality control over products and provide an avenue to achieve personalized medicine.”
Prof. Harrington explained “Our findings demonstrate that a chemotyping approach avoids the inherent pitfalls of genotyping and using plant morphology to identify and characterize cannabis products. In addition, we have demonstrated that low-cost methods such as UV spectroscopy can work just as well as more expensive high-resolution methods of nuclear magnetic resonance spectroscopy and MS.