Faculty and Student Publications
Document Type
Article
Publication Date
6-1-2021
Abstract
Fifty four domestically produced cannabis samples obtained from different USA states were quantitatively assayed by GC-FID to detect 22 active components: 15 terpenoids and 7 cannabinoids. The profiles of the selected compounds were used as inputs for samples grouping to their geographical origins and for building a geographical prediction model using Linear Discriminant Analysis. The proposed sample extraction and chromatographic separation was satisfactory to select 22 active ingredients with a wide analytical range between 5.0 and 1,000 μg/mL. Analysis of GC-profiles by Principle Component Analysis retained three significant variables for grouping job (Δ9-THC, CBN, and CBC) and the modest discrimination of samples based on their geographical origin was reported. PCA was able to separate many samples of Oregon and Vermont while a mixed classification was observed for the rest of samples. By using LDA as a supervised classification method, excellent separation of cannabis samples was attained leading to a classification of new samples not being included in the model. Using two principal components and LDA with GC-FID profiles correctly predict the geographical of 100% Washington cannabis, 86% of both Oregon and Vermont samples, and finally, 71% of Ohio samples.
Relational Format
journal article
Recommended Citation
Al Bakain, R. Z., Al-Degs, Y. S., Cizdziel, J. V., & Elsohly, M. A. (2021). Linear discriminant analysis based on gas chromatographic measurements for geographical prediction of USA medical domestic cannabis. Acta Chromatographica, 33(2), 179–187. https://doi.org/10.1556/1326.2020.00782
DOI
10.1556/1326.2020.00782
Accessibility Status
Searchable text