Abstract
Background Commercially available cannabis strains have multiplied in recent years as a consequence of regional changes in legislation for medicinal and recreational use. Lack of a standardized system to label plants and seeds hinders the consistent identification of particular strains with their elicited psychoactive effects. The objective of this work was to leverage information extracted from large databases to improve the identification and characterization of cannabis strains.
Methods We analyzed a large publicly available dataset where users freely reported their experiences with cannabis strains, including different subjective effects and flavour associations. This analysis was complemented with information on the chemical composition of a subset of the strains. Both supervised and unsupervised machine learning algorithms were applied to classify strains based on self-reported and objective features.
Results Metrics of strain similarity based on self-reported effect and flavour tags allowed machine learning classification into three major clusters corresponding to Cannabis sativa, Cannabis indica, and hybrids. Synergy between terpene and cannabinoid content was suggested by significative correlations between psychoactive effect and flavour tags. The use of predefined tags was validated by applying semantic analysis tools to unstructured written reviews, also providing breed-specific topics consistent with their purported medicinal and subjective effects. While cannabinoid content was variable even within individual strains, terpene profiles matched the perceptual characterizations made by the users and could be used to predict associations between different psychoactive effects.
Conclusions Our work represents the first data-driven synthesis of self-reported and chemical information in a large number of cannabis strains. Since terpene content is robustly inherited and less influenced by environmental factors, flavour perception could represent a reliable marker to predict the psychoactive effects of cannabis. Our novel methodology contributes to meet the demands for reliable strain classification and characterization in the context of an ever-growing market for medicinal and recreational cannabis.
Footnotes
1 We follow the notation where
refers to a matrix and Aij to a particular entry.2 As a naming convention, we identified sets of strains with a flavour in their name using that flavour, e.g. the group of “lemons” comprised strains such as Lemon Skunk and Lemon Diesel. We also described these groups by their general category, e.g. “lemons”, “grapefruits”, “strawberries” were labeled “fruits”.
Abbreviations
- THC
- Tetrahydrocannabinol
- CBD
- Cannabidiol
- AUC
- Area under the receiver operating characteristic curve
- LSA
- Latent Semantic Analysis
- SVD
- Singular Value Decomposition
- PCA
- Principal Component Analysis
- FDR
- False Discovery Rate.
