Author information
Abstract
Cannabis has been cultivated as a source of food, fiber, and medicine globally, so the classification of Cannabis cultivars based on their chemical fingerprints is important to standardize and control the quality of Cannabis, ensure that patients receive a full and consistent spectrum of therapeutic benefits, and promote the further implementation of Cannabis-based products in clinical uses. In this study, a high-throughput analytical method, thermal desorption direct analysis in real time mass spectrometry (TD-DART-MS), was employed to classify various Cannabis hemp cultivars with multivariate analysis. Cannabis plant materials from four cultivars were analyzed directly by TD-DART-MS without solvent extraction. The total run time was 15 min including 8 min for data acquisition and 7 min for cooling down the thermal stage. Data preprocessing strategy such as data transformation was evaluated on the TD-DART-MS data set and cubic root transform has shown significant improvement to the classification. TD-DART-MS data was then processed by principal component analysis (PCA) and the results were compared with those from liquid chromatography-mass spectrometry (LC-MS) data. The samples were clustered based on cultivars by PCA, and the validation samples collected 2 months later were also grouped together with the original samples by cultivars after mean-centering the data sets. Partial least squares discriminant analysis (PLS-DA) models were constructed with the TD-DART-MS data sets and a 99.3 ± 0.3% classification accuracy was obtained from 100 independent bootstrapped Latin partition evaluations. Our results indicate that TD-DART-MS may be used as a screening tool for the classification of Cannabis cultivars. Graphical abstract.
