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      Linear discriminant analysis based on gas chromatographic measurements for geographical prediction of USA medical domestic cannabis

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          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 (Δ 9-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.

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          Author and article information

          Acta Chromatographica
          Acta Chromatographica
          Akadémiai Kiadó (Budapest )
          19 March 2021
          05 June 2020
          : 33
          : 2
          : 179-187
          [1 ] Department of Chemistry, School of Science, The University of Jordan , P.O. Box 11942, Amman, Jordan
          [2 ] Chemistry Department, The Hashemite University , P.O. Box 150459, Zarqa, Jordan
          [3 ] Department of Chemistry and Biochemistry, University of Mississippi , University, MS, 38677, USA
          [4 ] National Center for Natural Products Research , University, MS, 38677-1848, USA
          [5 ] Department of Pharmaceutics and Drug Delivery, School of Pharmacy, University of Mississippi , University, MS, 38677, USA
          Author notes
          [* ]Corresponding author. Tel.: +962 6 535 5000 ext. 22138; Fax: +962 65300253 E-mail: r.bakain@ 123456ju.edu.jo
          Author information
          © 2020 The Authors

          Open Access statement. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium for non-commercial purposes, provided the original author and source are credited, a link to the CC License is provided, and changes – if any – are indicated.

          : 06 April 2020
          : 24 April 2020
          Page count
          Figures: 4, Tables: 3, Equations: 0, References: 22, Pages: 09
          Funded by: The Binational Fulbright Commission
          Funded by: The University of Jordan

          Materials properties,Nanomaterials,Chemistry,Nanotechnology,Analytical chemistry,Thin films & surfaces
          principal component analysis,geographical prediction,gas chromatography,medical cannabis,linear discriminant analysis


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