Use of near-infrared spectroscopy for the classification of medicinal cannabis cultivars and the prediction of their cannabinoid and terpene contents.

Cannabis sativa L. is used to treat a wide variety of medical conditions, in light of its beneficial pharmacological properties of its cannabinoids and terpenes. At present, the quantitative chemical analysis of these active compounds is achieved through the use of laborious, expensive, and time-consuming technologies, such as high-pressure liquid-chromatography- photodiode arrays, mass spectrometer detectors (HPLC-PDA or MS), or gas chromatography-mass spectroscopy (GC-MS). Hence, we aimed to develop a simple, accurate, fast, and cheap technique for the quantification of major cannabinoids and terpenes using Fourier transform near infra-red spectroscopy (FT-NIRS). FT-NIRS was coupled with multivariate classification and regression models, namely partial least square-discriminant analysis (PLS-DA) and partial least squares regression (PLS-R) models. The PLS-DA model yielded an absolute major class separation (high-THC, high-CBD, hybrid, and high-CBG) and perfect class prediction. Using only three latent variables (LVs), the cross-validation and prediction model errors indicated a low probability of over-fitting the data. In addition, the PLS-DA model enabled the classification of chemovars with genetic-chemical similarities. The classification of high-THCA chemovars was more sensitive and more specific than the classifications of the remaining chemovars. The prediction of cannabinoid and terpene concentrations by PLS-R yielded 11 robust models with high predictive capabilities (R2CV and R2pred > 0.8, RPD >2.5 and RPIQ >3, RMSECV/RMSEC ratio <1.2) and additional 15 models whose performance was acceptable for initial screening purposes (R2CV > 0.7 and R2pred < 0.8, RPD >2 and RPIQ <3, 1.2 < RMSECV/RMSEC ratio <2). Our results confirm that there is sufficient information in the FT-NIRS to develop cannabinoid and terpene prediction models and major-cultivar classification models.

Multivariate classification of cannabis chemovars based on their terpene and cannabinoid profiles.

Cannabis is used to treat various medical conditions, and lines are commonly classified according to their total concentrations of Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD). Based on their ratio of total THC to total CBD, cannabis cultivars are commonly classified into high-THC, high-CBD, and hybrid classes. While cultivars from the same class have similar compositions of major cannabinoids, their levels of other cannabinoids and their terpene compositions may differ substantially. Therefore, a more comprehensive and accurate classification of medicinal cannabis cultivars, based on a large number of cannabinoids and terpenes is needed. For this purpose, three different chemometric-based classification models were constructed using three sets of chemical profiles. We examined those models to determine which provides the most accurate "chemovar" classification. This was done by analyzing profiles of cannabinoids, terpenes, and the combination of these substances using the partial least square-discriminant analysis multivariate (PLS-DA) technique. The chemical profiles were selected from the three major classes of medicinal cannabis that are most commonly prescribed to patients in Israel: high-THC, high-cannabigerol (CBG), and hybrid. We studied the correlations between cannabinoids and terpenes to identify major bio-indicators representing the plant's terpene and cannabinoid content. All three PLS-DA models provided highly accurate classifications, utilizing six to nine latent variables with an overall accuracy ranging from 2 to 11% CV. The PLS-DA model applied to the combined cannabinoid-and-terpene profile did the best job of differentiating between the chemovars in terms of misclassification error, sensitivity, specificity, and accuracy. The combined cannabinoid-and-terpene PLS-DA profile had cross-validation and prediction misclassification errors of 4% and 0%, respectively. This is the first study to demonstrate the highly accurate classification of samples of medicinal cannabis based on their cannabinoid and terpene profiles, as compared to cannabinoid profiles alone. Furthermore, our correlation analysis indicated that 11 cannabinoids and terpenes might serve as bio-indicators for 32 different active compounds. These findings suggest that the use of multivariate statistics could assist in breeding studies and serve as a tool for minimizing the mislabeling of cannabis inflorescences.

Optimization of sweet basil harvest time and cultivar characterization using near-infrared spectroscopy, liquid and gas chromatography, and chemometric statistical methods.

BACKGROUND: Terpene, eugenol and polyphenolic contents of basil are major determinants of quality, which is affected by genetics, weather, growing practices, pests and diseases. Here, we aimed to develop a simple predictive analytical method for determining the polyphenol, eugenol and terpene content of the leaves of major Israeli sweet basil cultivars grown hydroponically, as a function of harvest time, through the use of near-infrared (NIR) spectroscopy, liquid/gas chromatography, and chemometric methods. We also wanted to identify the harvest time associated with the highest terpene, eugenol and polyphenol content. RESULTS: Six different cultivars and four different harvest times were analyzed. Partial least square regression (PLS-R) analysis yielded an accurate, predictive model that explained more than 93% of the population variance for all of the analyzed compounds. The model yielded good/excellent prediction (R2 > 0.90, R2 cv and R2 pre > 0.80) and very good residual predictive deviation (RPD > 2) for all of the analyzed compounds. Concentrations of rosmarinic acid, eugenol and terpenes increased steadily over the first 3 weeks, peaking in the fourth week in most of the cultivars. Our PLS-discriminant analysis (PLS-DA) model provided accurate harvest classification and prediction as compared to cultivar classification. The sensitivity, specificity and accuracy of harvest classification were larger than 0.82 for all harvest time points, whereas the cultivar classification, resulted in sensitivity values lower than 0.8 in three cultivars. CONCLUSION: The PLS-R model provided good predictions of rosmarinic acid, eugenol and terpene content. Our NIR coupled with a PLS-DA demonstrated reasonable solution for harvest and cultivar classification. © 2021 Society of Chemical Industry.