Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa.

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.

The Cannabis Plant as a Complex System: Interrelationships between Cannabinoid Compositions, Morphological, Physiological and Phenological Traits.

Maintaining specific and reproducible cannabinoid compositions (type and quantity) is essential for the production of cannabis-based remedies that are therapeutically effective. The current study investigates factors that determine the plant's cannabinoid profile and examines interrelationships between plant features (growth rate, phenology and biomass), inflorescence morphology (size, shape and distribution) and cannabinoid content. An examination of differences in cannabinoid profile within genotypes revealed that across the cultivation facility, cannabinoids' qualitative traits (ratios between cannabinoid quantities) remain fairly stable, while quantitative traits (the absolute amount of Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), cannabichromene (CBC), cannabigerol (CBG), Δ9-tetrahydrocannabivarin (THCV) and cannabidivarin (CBDV)) can significantly vary. The calculated broad-sense heritability values imply that cannabinoid composition will have a strong response to selection in comparison to the morphological and phenological traits of the plant and its inflorescences. Moreover, it is proposed that selection in favour of a vigorous growth rate, high-stature plants and wide inflorescences is expected to increase overall cannabinoid production. Finally, a range of physiological and phenological features was utilised for generating a successful model for the prediction of cannabinoid production. The holistic approach presented in the current study provides a better understanding of the interaction between the key features of the cannabis plant and facilitates the production of advanced plant-based medicinal substances.

A Simple High-Throughput Method for the Analysis of Vicine and Convicine in Faba Bean.

The faba bean is one of the earliest domesticated crops, with both economic and environmental benefits. Like most legumes, faba beans are high in protein, and can be used to contribute to a balanced diet, or as a meat substitute. However, they also produce the anti-nutritional compounds, vicine and convicine (v-c), that when enzymatically degraded into reactive aglycones can potentially lead to hemolytic anemia or favism. Current methods of analysis use LC-UV, but are only suitable at high concentrations, and thus lack the selectivity and sensitivity to accurately quantitate the low-v-c genotypes currently being developed. We have developed and fully validated a rapid high-throughput LC-MS method for the analysis of v-c in faba beans by optimizing the extraction protocol and assessing the method of linearity, limit of detection, limit of quantitation, accuracy, precision and matrix effects. This method uses 10-times less starting material; removes the use of buffers, acids and organic chemicals; and improves precision and accuracy when compared to current methods.

High-Throughput Quantitation of Cannabinoids by Liquid Chromatography Triple-Quadrupole Mass Spectrometry.

The high-throughput quantitation of cannabinoids is important for the cannabis industry. As medicinal products increase, and research into compounds that have pharmacological benefits increase, and the need to quantitate more than just the main cannabinoids becomes more important. This study aims to provide a rapid, high-throughput method for cannabinoid quantitation using a liquid chromatography triple-quadrupole mass spectrometer (LC-QQQ-MS) with an ultraviolet diode array detector (UV-DAD) for 16 cannabinoids: CBDVA, CBDV, CBDA, CBGA, CBG, CBD, THCV, THCVA, CBN, CBNA, THC, Δ8-THC, CBL, CBC, THCA-A and CBCA. Linearity, limit of detection (LOD), limit of quantitation (LOQ), accuracy, precision, recovery and matrix effect were all evaluated. The validated method was used to determine the cannabinoid concentration of four different Cannabis sativa strains and a low THC strain, all of which have different cannabinoid profiles. All cannabinoids eluted within five minutes with a total analysis time of eight minutes, including column re-equilibration. This was twice as fast as published LC-QQQ-MS methods mentioned in the literature, whilst also covering a wide range of cannabinoid compounds.

Use of Large and Diverse Datasets for 1H NMR Serum Metabolic Profiling of Early Lactation Dairy Cows.

Most livestock metabolomic studies involve relatively small, homogenous populations of animals. However, livestock farming systems are non-homogenous, and large and more diverse datasets are required to ensure that biomarkers are robust. The aims of this study were therefore to (1) investigate the feasibility of using a large and diverse dataset for untargeted proton nuclear magnetic resonance (1H NMR) serum metabolomic profiling, and (2) investigate the impact of fixed effects (farm of origin, parity and stage of lactation) on the serum metabolome of early-lactation dairy cows. First, we used multiple linear regression to correct a large spectral dataset (707 cows from 13 farms) for fixed effects prior to multivariate statistical analysis with principal component analysis (PCA). Results showed that farm of origin accounted for up to 57% of overall spectral variation, and nearly 80% of variation for some individual metabolite concentrations. Parity and week of lactation had much smaller effects on both the spectra as a whole and individual metabolites (< 3% and < 20%, respectively). In order to assess the effect of fixed effects on prediction accuracy and biomarker discovery, we used orthogonal partial least squares (OPLS) regression to quantify the relationship between NMR spectra and concentrations of the current gold standard serum biomarker of energy balance, ß-hydroxybutyrate (BHBA). Models constructed using data from multiple farms provided reasonably robust predictions of serum BHBA concentration (0.05 ≤ RMSE ≤ 0.18). Fixed effects influenced the results biomarker discovery; however, these impacts could be controlled using the proposed method of linear regression spectral correction.

A Simple LC-MS Method for the Quantitation of Alkaloids in Endophyte-Infected Perennial Ryegrass.

The rapid identification and quantitation of alkaloids produced by Epichloë endophyte-infected pasture grass is important for the agricultural industry. Beneficial alkaloids, such as peramine, provide the grass with enhanced insect protection. Conversely, ergovaline and lolitrem B can negatively impact livestock. Currently, a single validated method to measure these combined alkaloids in planta does not exist. Here, a simple two-step extraction method was developed for Epichloë-infected perennial ryegrass (Lolium perenne L.). Peramine, ergovaline and lolitrem B were quantified using liquid chromatography-mass spectrometry (LC-MS). Alkaloid linearity, limit of detection (LOD), limit of quantitation (LOQ), accuracy, precision, selectivity, recovery, matrix effect and robustness were all established. The validated method was applied to eight different ryegrass-endophyte symbiota. Robustness was established by comparing quantitation results across two additional instruments; a triple quadruple mass spectrometer (QQQ MS) and by fluorescence detection (FLD). Quantitation results were similar across all three instruments, indicating good reproducibility. LOQ values ranged from 0.8 ng/mL to 6 ng/mL, approximately one hundred times lower than those established by previous work using FLD (for ergovaline and lolitrem B), and LC-MS (for peramine). This work provides the first highly sensitive quantitative LC-MS method for the accurate and reproducible quantitation of important endophyte-derived alkaloids.

Development of a validated method for the qualitative and quantitative analysis of cannabinoids in plant biomass and medicinal cannabis resin extracts obtained by super-critical fluid extraction.

The social push for the therapeutic use of cannabis extracts has increased significantly over recent years. Cannabis is being used for treatment for conditions such as epilepsy, cancer and pain management. There are a range of medicinal cannabis products available, but the use of cannabis resin obtained by super critical fluid extraction, often diluted in oil, is becoming increasingly more prominent. Much of the research on cannabis has focused on plant biomass or the final therapeutic product with a concerning lack of information on the intermediate resin. This study aims to bridge the gap between current methods of analysis for biomass and the final therapeutic product by describing a fully developed and validated ultra-high-performance-liquid-chromatography method with diode array detection (UHPLC-DAD) for the qualification and quantification of the cannabinoids CBDA, CBD, CBN, THC, CBC and THCA, in medicinal cannabis biomass and resin obtained by super-critical fluid extraction (SFE). The method was validated for specificity, linearity, limit of detection (LOD), limit of quantitation (LOQ), precision, accuracy, robustness, spike recovery and stability in accordance with the Validation of Analytical Procedures: Text and Methodology Q2 to meet the requirements of the International Council for Harmonisation (ICH), Therapeutic Goods Authority (TGA) and the Food and Drug Administration (FDA) test method validation regulations.

Genetic modification of asexual Epichloë endophytes with the perA gene for peramine biosynthesis.

Development of grass-endophyte associations with minimal or no detrimental effects in combination with beneficial characteristics is important for pastoral agriculture. The feasibility of enhancing production of an endophyte-derived beneficial alkaloid through introduction of an additional gene copy was assessed in a proof-of-concept study. Sexual and asexual Epichloë species that form symbiotic associations with cool-season grasses of the Poaceae sub-family Pooideae produce bioactive alkaloids that confer resistance to herbivory by a number of organisms. Of these, peramine is thought to be crucial for protection of perennial ryegrass (Lolium perenne L.) from the Argentinian stem weevil, an economically important exotic pest in New Zealand, contributing significantly to pasture persistence. A single gene (perA) has been identified as solely responsible for peramine biosynthesis and is distributed widely across Epichloë taxa. In the present study, a functional copy of the perA gene was introduced into three recipient endophyte genomes by Agrobacterium tumefaciens-mediated transformation. The target strains included some that do not produce peramine, and others containing different perA gene copies. Mitotically stable transformants generated from all three endophyte strains were able to produce peramine in culture and in planta at variable levels. In summary, this study provides an insight into the potential for artificial combinations of alkaloid biosynthesis in a single endophyte strain through transgenesis, as well as the possibility of using novel genome editing techniques to edit the perA gene of non-peramine producing strains.

The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset.

Pain-related emotions are a major barrier to effective self rehabilitation in chronic pain. Automated coaching systems capable of detecting these emotions are a potential solution. This paper lays the foundation for the development of such systems by making three contributions. First, through literature reviews, an overview of how pain is expressed in chronic pain and the motivation for detecting it in physical rehabilitation is provided. Second, a fully labelled multimodal dataset (named 'EmoPain') containing high resolution multiple-view face videos, head mounted and room audio signals, full body 3D motion capture and electromyographic signals from back muscles is supplied. Natural unconstrained pain related facial expressions and body movement behaviours were elicited from people with chronic pain carrying out physical exercises. Both instructed and non-instructed exercises were considered to reflect traditional scenarios of physiotherapist directed therapy and home-based self-directed therapy. Two sets of labels were assigned: level of pain from facial expressions annotated by eight raters and the occurrence of six pain-related body behaviours segmented by four experts. Third, through exploratory experiments grounded in the data, the factors and challenges in the automated recognition of such expressions and behaviour are described, the paper concludes by discussing potential avenues in the context of these findings also highlighting differences for the two exercise scenarios addressed.