Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in <i>Cannabis Sativa</i>
Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestruc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2607 |
| _version_ | 1827752149292941312 |
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| author | Jonathan Tran Simone Vassiliadis Aaron C. Elkins Noel O. I. Cogan Simone J. Rochfort |
| author_facet | Jonathan Tran Simone Vassiliadis Aaron C. Elkins Noel O. I. Cogan Simone J. Rochfort |
| author_sort | Jonathan Tran |
| collection | DOAJ |
| description | Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography–mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II–Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4–100% accuracy prediction), the handheld device also performed well (83.1–100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material. |
| first_indexed | 2024-03-11T07:10:41Z |
| format | Article |
| id | doaj.art-a79bb180204647538e02e5d8fc7b6a0e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:10:41Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a79bb180204647538e02e5d8fc7b6a0e2023-11-17T08:37:15ZengMDPI AGSensors1424-82202023-02-01235260710.3390/s23052607Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in <i>Cannabis Sativa</i>Jonathan Tran0Simone Vassiliadis1Aaron C. Elkins2Noel O. I. Cogan3Simone J. Rochfort4Agriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, AustraliaAgriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, AustraliaAgriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, AustraliaAgriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, AustraliaAgriculture Victoria Research, AgriBio Centre, AgriBio, Melbourne, VIC 3083, AustraliaCannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography–mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II–Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4–100% accuracy prediction), the handheld device also performed well (83.1–100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.https://www.mdpi.com/1424-8220/23/5/2607cannabinoidspartial least square regressionpartial least square discriminant analysisprincipal component analysis |
| spellingShingle | Jonathan Tran Simone Vassiliadis Aaron C. Elkins Noel O. I. Cogan Simone J. Rochfort Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in <i>Cannabis Sativa</i> Sensors cannabinoids partial least square regression partial least square discriminant analysis principal component analysis |
| title | Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in <i>Cannabis Sativa</i> |
| title_full | Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in <i>Cannabis Sativa</i> |
| title_fullStr | Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in <i>Cannabis Sativa</i> |
| title_full_unstemmed | Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in <i>Cannabis Sativa</i> |
| title_short | Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in <i>Cannabis Sativa</i> |
| title_sort | developing prediction models using near infrared spectroscopy to quantify cannabinoid content in i cannabis sativa i |
| topic | cannabinoids partial least square regression partial least square discriminant analysis principal component analysis |
| url | https://www.mdpi.com/1424-8220/23/5/2607 |
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