Improved Long-Term Preservation of Cannabis Inflorescence by Utilizing Integrated Pre-Harvest Hexanoic Acid Treatment and Optimal Post-Harvest Storage Conditions.

The effort to maintain cannabinoid and terpene levels in harvested medicinal cannabis inflorescence is crucial, as many studies demonstrated a significant concentration decrease in these compounds during the drying, curing, and storage steps. These stages are critical for the preparation and preservation of medicinal cannabis for end-use, and any decline in cannabinoid and terpene content could potentially reduce the therapeutic efficacy of the product. Consequently, in the present study, we determined the efficacy of pre-harvest hexanoic acid treatment alongside four months of post-harvest vacuum storage in prolonging the shelf life of high THCA cannabis inflorescence. Our findings indicate that hexanoic acid treatment led to elevated concentrations of certain cannabinoids and terpenes on the day of harvest and subsequent to the drying and curing processes. Furthermore, the combination of hexanoic acid treatment and vacuum storage yielded the longest shelf life and the highest cannabinoid and mono-terpene content as compared to all other groups studied. Specifically, the major cannabinoid's-(-)-Δ9-trans-tetrahydrocannabinolic acid (THCA)-concentration decreased by 4-23% during the four months of storage with the lowest reduction observed following hexanoic acid pre-harvest treatment and post-harvest vacuum storage. Hexanoic acid spray application displayed a more pronounced impact on mono-terpene preservation than storage under vacuum without hexanoic acid treatment. Conversely, sesqui-terpenes were observed to be less prone to degradation than mono-terpenes over an extended storage duration. In summation, appropriate pre-harvest treatment coupled with optimized storage conditions can significantly extend the shelf life of cannabis inflorescence and preserve high active compound concentration over an extended time period.

In Pursuit of Optimal Quality: Cultivar-Specific Drying Approaches for Medicinal Cannabis.

A limited number of studies have examined how drying conditions affect the cannabinoid and terpene content in cannabis inflorescences. In the present study, we evaluated the potential of controlled atmosphere drying chambers for drying medicinal cannabis inflorescence. Controlled atmosphere drying chambers were found to reduce the drying and curing time by at least 60% compared to traditional drying methods, while preserving the volatile terpene content. On the other hand, inflorescences subjected to traditional drying were highly infested by Alternaria alternata and also revealed low infestation of Botrytis cinerea. In the high-THC chemovar ("240"), controlled N2 and atm drying conditions preserved THCA concentration as compared to the initial time point (t0). On the other hand, in the hybrid chemovar ("Gen12") all of the employed drying conditions preserved THCA and CBDA content. The optimal drying conditions for preserving monoterpenes and sesquiterpenes in both chemovars were C5O5 (5% CO2, 5% O2, and 90% N2) and pure N2, respectively. The results of this study suggest that each chemovar may require tailored drying conditions in order to preserve specific terpenes and cannabinoids. Controlled atmosphere drying chambers could offer a cost-effective, fast, and efficient drying method for preserving cannabinoids and terpenes during the drying process while reducing the risk of mold growth.

Novel fluorescence spectroscopy method coupled with N-PLS-R and PLS-DA models for the quantification of cannabinoids and the classification of cannabis cultivars.

INTRODUCTION: Cannabis sativa L. inflorescences are rich in secondary metabolites, particularly cannabinoids. The most common techniques for elucidating cannabinoid composition are expensive technologies, such as high-pressure liquid chromatography (HPLC). OBJECTIVES: We aimed to develop and evaluate the performance of a novel fluorescence spectroscopy-based method coupled with N-way partial least squares regression (N-PLS-R) and partial least squares discriminant analysis (PLS-DA) models to replace the expensive chromatographic methods for preharvest cannabinoid quantification. METHODOLOGY: Fresh medicinal cannabis inflorescences were collected and ethanol extracts were prepared. Their excitation-emission spectra were measured using fluorescence spectroscopy and their cannabinoid contents were determined by HPLC-PDA. Subsequently, N-PLS-R and PLS-DA models were applied to the excitation-emission matrices (EEMs) for cannabinoid concentration prediction and cultivar classification, respectively. RESULTS: The N-PLS-R model was based on a set of EEMs (n = 82) and provided good to excellent quantification of (-)-Δ9-trans-tetrahydrocannabinolic acid, cannabidiolic acid, cannabigerolic acid, cannabichromenic acid, and (-)-Δ9-trans-tetrahydrocannabinol (R2 CV and R2 pred  > 0.75; RPD > 2.3 and RPIQ > 3.5; RMSECV/RMSEC ratio < 1.4). The PLS-DA model enabled a clear distinction between the four major classes studied (sensitivity, specificity, and accuracy of the prediction sets were all ≥0.9). CONCLUSIONS: The fluorescence spectral region (excitation 220-400 nm, emission 280-550 nm) harbors sufficient information for accurate prediction of cannabinoid contents and accurate classification using a relatively small data set.

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.

Depletion kinetics and concentration- and time-dependent toxicity of a tertiary mixture of amitraz and its major hydrolysis products in honeybees.

Although amitraz is one of the acaricides most commonly applied within beehives, to date, its time-dependent oral toxicity in honeybees has not been investigated, due to amitraz's instability in aqueous media. In aqueous media such as honey, amitraz rapidly forms a continuously changing tertiary mixture with two of its major hydrolysis products, DMF and DMPF. The contribution of each hydrolysis product to the overall oral toxicity of this acaricide is not known. Therefore, we aimed to characterize the depletion and formation kinetics of amitraz and its hydrolysis products in 50% sucrose solution provided to caged honeybees, including the calculation of the 50% lethal oral concentration (LC50) of amitraz. We sought to determine the contribution of each component of the mixture to the overall observed toxicity. We also investigated the time- and concentration-dependent toxicity of the amitraz mixture and its hydrolysis products. A novel approach based on the analysis of the areas under the depletion and formation curves of amitraz and its hydrolysis products revealed that DMPF, amitraz and DMF accounted for 92%, 7% and 1% (respectively) of the overall toxicity of the mixture. The chronic oral LC50 of amitraz was 3300 µmol/L, of similar magnitude as that of the non-toxic hydrolysis product DMF. The toxicity of DMPF and the mixture decreased over time; whereas the toxicity of DMF increased over time. Amitraz's instability in aqueous media and the highly toxic profile of DMPF, suggest that DMPF is the actual toxic entity responsible for amitraz's toxicity toward honeybees.

Concentration- and time-dependent toxicity of commonly encountered pesticides and pesticide mixtures to honeybees (Apis mellifera L.).

Honeybees are exposed to a wide range of pesticides for long periods via contaminated water, pollen and nectar. Some of those pesticides might constitute health hazards in a time- and dose-dependent manner. Time-dependent toxicity profiles for many applied pesticides are lacking, despite the fact that such profiles are crucial for toxicological evaluations. Therefore, we sought to determine the time-dependent toxicities of pesticides/pesticide metabolites frequently found in Israeli beehives, namely, amitraz metabolites, N'-(2,4-dimethylphenyl)-N-methylformamidine (DMPF) and N-(2,4-dimethylphenyl)-formamide (DMF), coumaphos, imidacloprid, thiacloprid, acetamiprid and dimethoate (toxic reference). By applying accepted methodological approaches such as the modified Haber's rule (product of concentration and exposure duration leads to a constant effect) and comparisons between cumulative doses at different time points, we determined the time-dependent toxicities of these pesticides. We also studied the mixture toxicities of frequently occurring pesticide combinations and estimated their potential contributions to the overall toxicities of neonicotinoids. Thiacloprid was the only pesticide that complied with Haber's rule. DMPF, dimethoate and imidacloprid exhibited time-diminished -toxicities. In contrast, DMF and acetamiprid exhibited time-reinforced toxicities. Neither the binary mixtures nor the tertiary mixtures of DMF, DMPF and coumaphos at 10 times their environmentally relevant concentrations potentiated the neonicotinoids' toxicities. DMPF and imidacloprid were found to present the greatest hazard to honeybees, based on their 50% lethal cumulative dose and 50% lethal time. Amitraz's instability, its low detection frequency and high toxicity profile of its metabolite, DMPF, lead us to the conclusion that DMPF constitutes the actual toxic entity responsible for amitraz's toxic effect.