-
Something wrong with this record ?
Gas chromatographic analysis of naturally occurring cannabinoids: A review of literature published during the past decade
L. Nahar, M. Guo, SD. Sarker,
Language English Country Great Britain
Document type Journal Article, Review
Grant support
CZ.02.1.01/0.0/0.0/16_019/0000868
European Regional Development Fund - Project ENOCH
PubMed
31469459
DOI
10.1002/pca.2886
Knihovny.cz E-resources
- MeSH
- Cannabis * MeSH
- Cannabinoids * MeSH
- Gas Chromatography-Mass Spectrometry MeSH
- Tandem Mass Spectrometry MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
INTRODUCTION: Cannabinoids are organic compounds, natural or synthetic, that bind to the cannabinoid receptors and have similar pharmacological properties as produced by the cannabis plant, Cannabis sativa. Gas chromatography (GC), e.g. gas chromatography mass spectrometry (GC-MS), is a popular analytical tool that has been used extensively to analyse cannabinoids in various matrices. OBJECTIVE: To review published literature on the use of various GC-based analytical methods for the analysis of naturally occurring cannabinoids published during the past decade. METHODOLOGY: A comprehensive literature search was performed utilising several databases, like Web of Knowledge, PubMed and Google Scholar, and other relevant published materials including published books. The keywords used, in various combinations, with cannabinoids being present in all combinations, in the search were cannabinoids, Cannabis sativa, marijuana, analysis, GC, quantitative, qualitative and quality control. RESULTS: During the past decade, several GC-based methods for the analysis of cannabinoids have been reported. While simple one-dimensional (1D) GC-MS and GC-FID (flame ionisation detector) methods were found to be quite common in cannabinoids analysis, two-dimensional (2D) GC-MS as well as GC-MS/MS also were popular because of their ability to provide more useful data for identification and quantification of cannabinoids in various matrices. Some degree of automation in sample preparation, and applications of mathematical and computational models for optimisation of different protocols were observed, and pre-analyses included various derivatisation techniques, and environmentally friendly extraction protocols. CONCLUSIONS: GC-based analysis of naturally occurring cannabinoids, especially using GC-MS, has dominated the cannabinoids analysis in the last decade; new derivatisation methods, new ionisation methods, and mathematical models for method optimisation have been introduced.
References provided by Crossref.org
- 000
- 00000naa a2200000 a 4500
- 001
- bmc20005965
- 003
- CZ-PrNML
- 005
- 20200521091635.0
- 007
- ta
- 008
- 200511s2020 xxk f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1002/pca.2886 $2 doi
- 035 __
- $a (PubMed)31469459
- 040 __
- $a ABA008 $b cze $d ABA008 $e AACR2
- 041 0_
- $a eng
- 044 __
- $a xxk
- 100 1_
- $a Nahar, Lutfun $u Laboratory of Growth Regulators, Institute of Experimental Botany ASCR & Palacký University, Olomouc, Czech Republic.
- 245 10
- $a Gas chromatographic analysis of naturally occurring cannabinoids: A review of literature published during the past decade / $c L. Nahar, M. Guo, SD. Sarker,
- 520 9_
- $a INTRODUCTION: Cannabinoids are organic compounds, natural or synthetic, that bind to the cannabinoid receptors and have similar pharmacological properties as produced by the cannabis plant, Cannabis sativa. Gas chromatography (GC), e.g. gas chromatography mass spectrometry (GC-MS), is a popular analytical tool that has been used extensively to analyse cannabinoids in various matrices. OBJECTIVE: To review published literature on the use of various GC-based analytical methods for the analysis of naturally occurring cannabinoids published during the past decade. METHODOLOGY: A comprehensive literature search was performed utilising several databases, like Web of Knowledge, PubMed and Google Scholar, and other relevant published materials including published books. The keywords used, in various combinations, with cannabinoids being present in all combinations, in the search were cannabinoids, Cannabis sativa, marijuana, analysis, GC, quantitative, qualitative and quality control. RESULTS: During the past decade, several GC-based methods for the analysis of cannabinoids have been reported. While simple one-dimensional (1D) GC-MS and GC-FID (flame ionisation detector) methods were found to be quite common in cannabinoids analysis, two-dimensional (2D) GC-MS as well as GC-MS/MS also were popular because of their ability to provide more useful data for identification and quantification of cannabinoids in various matrices. Some degree of automation in sample preparation, and applications of mathematical and computational models for optimisation of different protocols were observed, and pre-analyses included various derivatisation techniques, and environmentally friendly extraction protocols. CONCLUSIONS: GC-based analysis of naturally occurring cannabinoids, especially using GC-MS, has dominated the cannabinoids analysis in the last decade; new derivatisation methods, new ionisation methods, and mathematical models for method optimisation have been introduced.
- 650 12
- $a kanabinoidy $7 D002186
- 650 12
- $a Cannabis $7 D002188
- 650 _2
- $a plynová chromatografie s hmotnostně spektrometrickou detekcí $7 D008401
- 650 _2
- $a tandemová hmotnostní spektrometrie $7 D053719
- 655 _2
- $a časopisecké články $7 D016428
- 655 _2
- $a přehledy $7 D016454
- 700 1_
- $a Guo, Mingquan $u Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China. Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan, China.
- 700 1_
- $a Sarker, Satyajit D $u Centre for Natural Products Discovery, School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK.
- 773 0_
- $w MED00005683 $t Phytochemical analysis : PCA $x 1099-1565 $g Roč. 31, č. 2 (2020), s. 135-146
- 856 41
- $u https://pubmed.ncbi.nlm.nih.gov/31469459 $y Pubmed
- 910 __
- $a ABA008 $b sig $c sign $y a $z 0
- 990 __
- $a 20200511 $b ABA008
- 991 __
- $a 20200521091632 $b ABA008
- 999 __
- $a ok $b bmc $g 1524823 $s 1096021
- BAS __
- $a 3
- BAS __
- $a PreBMC
- BMC __
- $a 2020 $b 31 $c 2 $d 135-146 $e 20190830 $i 1099-1565 $m Phytochemical analysis $n Phytochem Anal $x MED00005683
- GRA __
- $a CZ.02.1.01/0.0/0.0/16_019/0000868 $p European Regional Development Fund - Project ENOCH
- LZP __
- $a Pubmed-20200511