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. 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.

Why not XY? Male monoecious sexual phenotypes challenge the female monoecious paradigm in Cannabis sativa L.

“…The IPK_CAN_36 accession is of an unknown origin, and phylogenetic studies do not group it with other European and Asian accessions from the IPK collection ( Woods et al., 2023 ). Previous metabolite analysis and phenotypic analysis would suggest it is a small-statured, highly branched Type 1 land race ( Gloerfelt-Tarp et al., 2023 ). The first monoecious individual was isolated from a group of 20 plants containing 12 pure males and 7 pure females.…”

Light and Shadow in Near-Infrared Spectroscopy: A Powerful Tool for Cannabis sativa L. Analysis

“…As for Cannabis applications, there are few in which ANN models are applied. For example, Gloerfelt-Tarp et al [47] developed a NIR-based chemometric application for the quantification of 12 cannabinoids in plant material, with emphasis on the discrimination between neutral and carboxylic forms of each cannabinoid. For this purpose, different machine learning algorithms were employed, including deep neural network and random forest, affording values of root mean standard error of validation (RMSE v ) in the range of 0.001 and 0.560 (%) (Table 2).…”

“…Therefore, NIRS is truly in line with the green analytical chemistry principles, i.e., diminishing the utilization of hazardous chemicals and reagents, employing equipment that is energy-efficient, and producing minimal waste [46]. However, the main drawback of NIRS technology is the interpretation of the spectra, which is difficult, and the use of mathematical and statistical methods, i.e., chemometrics, is mandatory to extract the important information related to chemical mechanisms [47,48].…”

Non-Destructive Near-Infrared Technology for Efficient Cannabinoid Analysis in Cannabis Inflorescences

“…The analysis of NIR spectra entails comparing data with a reference method such as HPLC to construct predictive models employing statistical methodologies [ 15 ]. To predict the cannabinoids of different cannabis cultivars accurately, it is essential to have access to extensive and diverse sample sets that adequately represent the genetic and corresponding chemotypic diversity [ 16 ]. While existing methods often involve scanning ground or semi-ground inflorescence samples, a limited number of studies have explored the potential of NIR spectroscopy on whole inflorescence samples [ 10 , 13 ].…”