Abstract
As the use of Cannabis products as natural medicines burgeons, it is also appearing as a food ingredient. It is important to screen Cannabis samples as ingredients by profiling their chemical compositions, which is referred to as chemotyping. Two sets of botanical extracts were studied. The first set is referred to as Cannabis contained plant materials from 15 samples of the sativa, indica, and hybrids of the two species. The second set contained 20 extracts from the variety of Cannabis sativa with low tetrahydrocannabinol (THC) concentrations, i.e., below 0.3%, and, henceforth, will be referred to as hemp. An ultraviolet (UV) microplate reader provides a cost-effective and high-throughput method for identifying chemotypes of plant extracts by their spectra. The microplate reader affords rapid measurements of small volumes, e.g., 50 µL, which demonstrates a potential to significantly reduce the analysis time and cost for Cannabis and hemp chemotyping or chemical profiling. Replicate samples were measured on different days to demonstrate the robustness of the method. Projected difference resolution (PDR) maps were used to visualize the separations among the classes. Five multivariate classifiers, fuzzy rule-building expert system (FuRES), super partial least squares-discriminant analysis (sPLS-DA), support vector machine (SVM), and two tree-based support vector machines (SVMtreeG and SVMtreeH) were evaluated. The classifiers were validated with ten bootstrapped Latin partitions (BLPs). For the Cannabis extracts, the SVMtreeG yielded the best performance and the classification accuracy was 99.1 ± 0.4% for spectra collected in the nonlinear absorbance range. For the hemp extracts, the SVM classifier performed the best with a 97.4 ± 0.6% classification accuracy. These results demonstrate that the UV microplate reader coupled with multivariate classifiers can be used as a high-throughput and cost-effective approach for chemotyping Cannabis.
Published Version
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