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Identification of Terpenoid Chemotypes Among High (−)-trans-Δ(9)- Tetrahydrocannabinol-Producing Cannabis sativa L. Cultivars

Introduction: With laws changing around the world regarding the legal status of Cannabis sativa (cannabis) it is important to develop objective classification systems that help explain the chemical variation found among various cultivars. Currently cannabis cultivars are named using obscure and inco...

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Autor principal: Fischedick, Justin T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Mary Ann Liebert, Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436332/
https://www.ncbi.nlm.nih.gov/pubmed/28861503
http://dx.doi.org/10.1089/can.2016.0040
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author Fischedick, Justin T.
author_facet Fischedick, Justin T.
author_sort Fischedick, Justin T.
collection PubMed
description Introduction: With laws changing around the world regarding the legal status of Cannabis sativa (cannabis) it is important to develop objective classification systems that help explain the chemical variation found among various cultivars. Currently cannabis cultivars are named using obscure and inconsistent nomenclature. Terpenoids, responsible for the aroma of cannabis, are a useful group of compounds for distinguishing cannabis cultivars with similar cannabinoid content. Methods: In this study we analyzed terpenoid content of cannabis samples obtained from a single medical cannabis dispensary in California over the course of a year. Terpenoids were quantified by gas chromatography with flame ionization detection and peak identification was confirmed with gas chromatography mass spectrometry. Quantitative data from 16 major terpenoids were analyzed using hierarchical clustering analysis (HCA), principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Results: A total of 233 samples representing 30 cultivars were used to develop a classification scheme based on quantitative data, HCA, PCA, and OPLS-DA. Initially cultivars were divided into five major groups, which were subdivided into 13 classes based on differences in terpenoid profile. Different classification models were compared with PLS-DA and found to perform best when many representative samples of a particular class were included. Conclusion: A hierarchy of terpenoid chemotypes was observed in the data set. Some cultivars fit into distinct chemotypes, whereas others seemed to represent a continuum of chemotypes. This study has demonstrated an approach to classifying cannabis cultivars based on terpenoid profile.
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spelling pubmed-54363322017-08-31 Identification of Terpenoid Chemotypes Among High (−)-trans-Δ(9)- Tetrahydrocannabinol-Producing Cannabis sativa L. Cultivars Fischedick, Justin T. Cannabis Cannabinoid Res Original Research Introduction: With laws changing around the world regarding the legal status of Cannabis sativa (cannabis) it is important to develop objective classification systems that help explain the chemical variation found among various cultivars. Currently cannabis cultivars are named using obscure and inconsistent nomenclature. Terpenoids, responsible for the aroma of cannabis, are a useful group of compounds for distinguishing cannabis cultivars with similar cannabinoid content. Methods: In this study we analyzed terpenoid content of cannabis samples obtained from a single medical cannabis dispensary in California over the course of a year. Terpenoids were quantified by gas chromatography with flame ionization detection and peak identification was confirmed with gas chromatography mass spectrometry. Quantitative data from 16 major terpenoids were analyzed using hierarchical clustering analysis (HCA), principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Results: A total of 233 samples representing 30 cultivars were used to develop a classification scheme based on quantitative data, HCA, PCA, and OPLS-DA. Initially cultivars were divided into five major groups, which were subdivided into 13 classes based on differences in terpenoid profile. Different classification models were compared with PLS-DA and found to perform best when many representative samples of a particular class were included. Conclusion: A hierarchy of terpenoid chemotypes was observed in the data set. Some cultivars fit into distinct chemotypes, whereas others seemed to represent a continuum of chemotypes. This study has demonstrated an approach to classifying cannabis cultivars based on terpenoid profile. Mary Ann Liebert, Inc. 2017-03-01 /pmc/articles/PMC5436332/ /pubmed/28861503 http://dx.doi.org/10.1089/can.2016.0040 Text en © Justin T. Fischedick 2017; Published by Mary Ann Liebert, Inc. This Open Access article is distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
spellingShingle Original Research
Fischedick, Justin T.
Identification of Terpenoid Chemotypes Among High (−)-trans-Δ(9)- Tetrahydrocannabinol-Producing Cannabis sativa L. Cultivars
title Identification of Terpenoid Chemotypes Among High (−)-trans-Δ(9)- Tetrahydrocannabinol-Producing Cannabis sativa L. Cultivars
title_full Identification of Terpenoid Chemotypes Among High (−)-trans-Δ(9)- Tetrahydrocannabinol-Producing Cannabis sativa L. Cultivars
title_fullStr Identification of Terpenoid Chemotypes Among High (−)-trans-Δ(9)- Tetrahydrocannabinol-Producing Cannabis sativa L. Cultivars
title_full_unstemmed Identification of Terpenoid Chemotypes Among High (−)-trans-Δ(9)- Tetrahydrocannabinol-Producing Cannabis sativa L. Cultivars
title_short Identification of Terpenoid Chemotypes Among High (−)-trans-Δ(9)- Tetrahydrocannabinol-Producing Cannabis sativa L. Cultivars
title_sort identification of terpenoid chemotypes among high (−)-trans-δ(9)- tetrahydrocannabinol-producing cannabis sativa l. cultivars
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436332/
https://www.ncbi.nlm.nih.gov/pubmed/28861503
http://dx.doi.org/10.1089/can.2016.0040
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