Classifying Cannabis sativa chemovars using K-means analysis of elemental composition

Understanding specific nutritional requirements of plants is a necessary component of growth optimization and can help growers promote desired traits and mitigate undesired traits. Cannabis is one such medicinally valuable plant prized for its content of specific phytochemicals. Cannabis is a commodity with numerous chemovars, each with their own nutritional needs. This can make generalizing targets for elemental content in the finished plant commodity difficult. One approach to make better informed decisions about cannabis plant nutrition is the application of chemometrics. In the current study, thirteen elements were quantitatively measured in the leaves of cannabis plants using an Agilent Inductively Coupled Plasma Mass Spectroscopy and an Elementar Vario Macro Cube. Correlation analysis, principal components analysis, and K-means clustering were utilized to describe and elucidate trends in the dataset. Moderately positive, monotonic correlations were found between magnesium, boron, and calcium, along with nitrogen, sulfur, and copper. PCA was used to corroborate these relationships. Clustering analysis was able to identify three distinct groups to which strains could be mapped with a relatively high degree of resolution when compared to cultivator identifiers. These findings suggest similar methods of introduction and elemental incorporation into the strains of these distinct groups. The method utilized in the current study demonstrate the ability of naïve clustering analysis to isolate differences in elemental concentrations between strains, allowing for the identification of unique cannabis chemovars. Such a process may be used to guide cultivation by classifying strains based on inherent nutritional requirements.