Characterization of Cannabis Cultivars Using Public Testing Data

Cannabis cultivars are typically identified by name, yet the extent to which cultivar names correspond to distinct chemical profiles remains largely unexamined at scale. Public cannabis compliance testing datasets provide an opportunity to evaluate cultivars as measurable chemical populations using large numbers of laboratory results collected across regulated markets.

This presentation introduces a data-driven framework for characterizing cannabis cultivars using multi-state testing data, with a focus on terpene composition and relationships between compounds. Using statewide datasets obtained through public records requests, terpene relationships that best distinguish cultivars from one another are identified by measuring separation, overlap, and variability across samples. These relationships allow cultivars to be evaluated as chemical populations rather than single measurements.

The framework can incorporate additional cultivars as new data becomes available, enabling continuous comparison across large datasets and refinement of cultivar characterization. This approach also allows evaluation of how consistently products sharing the same cultivar name occupy similar chemical space.

Applications include assessing chemical distinctness between cultivars, examining the consistency of cultivar labeling across products, and monitoring how cultivar-associated chemical signatures appear across markets. The results illustrate how public testing data can support evidence-based approaches to cultivar characterization and broader chemical analysis of cannabis products.

Learning Objectives:

1. Describe how large cannabis testing datasets can be used to characterize cultivars based on terpene composition.

2. Analyze relationships between terpene compounds to evaluate chemical distinctness between cannabis cultivars.

3. Evaluate how laboratory testing data can be used to assess consistency among products sharing the same cultivar name.