Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantification
Abstract C. sativa has gained renewed interest as a cash crop for food, fibre and medicinal markets. Irrespective of the final product, rigorous quantitative testing for cannabinoids, the regulated biologically active constituents of C. sativa, is a legal prerequisite across the supply chains. Curre...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-29148-0 |
| _version_ | 1811165932115984384 |
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| author | Francine Gloerfelt-Tarp Amitha K. Hewavitharana Jos Mieog William M. Palmer Felicity Fraser Omid Ansari Tobias Kretzschmar |
| author_facet | Francine Gloerfelt-Tarp Amitha K. Hewavitharana Jos Mieog William M. Palmer Felicity Fraser Omid Ansari Tobias Kretzschmar |
| author_sort | Francine Gloerfelt-Tarp |
| collection | DOAJ |
| description | Abstract C. sativa has gained renewed interest as a cash crop for food, fibre and medicinal markets. Irrespective of the final product, rigorous quantitative testing for cannabinoids, the regulated biologically active constituents of C. sativa, is a legal prerequisite across the supply chains. Currently, the medicinal cannabis and industrial hemp industries depend on costly chromatographic analysis for cannabinoid quantification, limiting production, research and development. Combined with chemometrics, Near-InfraRed spectroscopy (NIRS) has potential as a rapid, accurate and economical alternative method for cannabinoid analysis. Using chromatographic data on 12 therapeutically relevant cannabinoids together with spectral output from a diffuse reflectance NIRS device, predictive chemometric models were built for major and minor cannabinoids using dried, homogenised C. sativa inflorescences from a diverse panel of 84 accessions. Coefficients of determination (r2) of the validation models for 10 of the 12 cannabinoids ranged from 0.8 to 0.95, with models for major cannabinoids showing best performance. NIRS was able to discriminate between neutral and acidic forms of cannabinoids as well as between C3-alkyl and C5-alkyl cannabinoids. The results show that NIRS, when used in conjunction with chemometrics, is a promising method to quantify cannabinoids in raw materials with good predictive results. |
| first_indexed | 2024-04-10T15:44:14Z |
| format | Article |
| id | doaj.art-79aebb4deb684709afade6e9499d9e74 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T15:44:14Z |
| publishDate | 2023-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-79aebb4deb684709afade6e9499d9e742023-02-12T12:13:08ZengNature PortfolioScientific Reports2045-23222023-02-0113111410.1038/s41598-023-29148-0Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantificationFrancine Gloerfelt-Tarp0Amitha K. Hewavitharana1Jos Mieog2William M. Palmer3Felicity Fraser4Omid Ansari5Tobias Kretzschmar6Southern Cross UniversitySouthern Cross UniversitySouthern Cross UniversityResearch Division, Rapid Phenotyping (Hone)Research Division, Rapid Phenotyping (Hone)Ecofibre LtdSouthern Cross UniversityAbstract C. sativa has gained renewed interest as a cash crop for food, fibre and medicinal markets. Irrespective of the final product, rigorous quantitative testing for cannabinoids, the regulated biologically active constituents of C. sativa, is a legal prerequisite across the supply chains. Currently, the medicinal cannabis and industrial hemp industries depend on costly chromatographic analysis for cannabinoid quantification, limiting production, research and development. Combined with chemometrics, Near-InfraRed spectroscopy (NIRS) has potential as a rapid, accurate and economical alternative method for cannabinoid analysis. Using chromatographic data on 12 therapeutically relevant cannabinoids together with spectral output from a diffuse reflectance NIRS device, predictive chemometric models were built for major and minor cannabinoids using dried, homogenised C. sativa inflorescences from a diverse panel of 84 accessions. Coefficients of determination (r2) of the validation models for 10 of the 12 cannabinoids ranged from 0.8 to 0.95, with models for major cannabinoids showing best performance. NIRS was able to discriminate between neutral and acidic forms of cannabinoids as well as between C3-alkyl and C5-alkyl cannabinoids. The results show that NIRS, when used in conjunction with chemometrics, is a promising method to quantify cannabinoids in raw materials with good predictive results.https://doi.org/10.1038/s41598-023-29148-0 |
| spellingShingle | Francine Gloerfelt-Tarp Amitha K. Hewavitharana Jos Mieog William M. Palmer Felicity Fraser Omid Ansari Tobias Kretzschmar Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantification Scientific Reports |
| title | Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantification |
| title_full | Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantification |
| title_fullStr | Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantification |
| title_full_unstemmed | Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantification |
| title_short | Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantification |
| title_sort | using a global diversity panel of cannabis sativa l to develop a near infrared based chemometric application for cannabinoid quantification |
| url | https://doi.org/10.1038/s41598-023-29148-0 |
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