The potential of near infrared spectroscopy to estimate the content of cannabinoids in Cannabis sativa L.: A comparative study

Characterization of CBD oils, seized on the Belgian market, using infrared spectroscopy: Matrix identification and CBD determination, a proof of concept

The availability of cannabidiol (CBD) oil products has increased in recent years. No analytical controls are mandatory for these products leading to uncertainties about composition and quality. In this paper, a methodology was developed to identify the oil matrix and to estimate the CBD content in such samples, using mid-infrared and near-infrared spectroscopy. Different oils were selected based on the information labeled on products and were bought in food stores in order to create a sample set with a variety of matrices. These oils were spiked with CBD to obtain samples with CBD levels from 0% to 20%. The first part of the study was focused on the qualitative analysis of the oil matrix. A classification model, based on Soft Independent Modeling of Class Analogy, was build using the spiked oils to distinguish between the different oil matrices. For both spectroscopic techniques, the sensitivity, the specificity, the accuracy and the precision were equal to 100%. These models were applied to determine the oil matrix of seized samples. The second part of the study was focused on the quantitative estimation of CBD. After determination of CBD in seized samples using gas chromatography-tandem mass spectrometry, partial least square regression (PLS-R) models were built, one for each matrix in the sample set. Both techniques were able to classify unknown oily samples according to their matrix, and although only few samples were available to evaluate the PLS-R models, the approach clearly showed promising results for the estimation of the CBD content in oil samples.

Measurement and risk perception of non-ionizing radiation from base transceiver stations in Dhaka City of Bangladesh

Multiple harmful health effects can have on the population from non-ionizing radiation (NIR) sources. To date, there has been no extensive data collection about NIR emitted from base transceiver stations in Dhaka City, Bangladesh. This study aims to remedy that by collecting data and comparing the processed data to the international standards, International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines, and standards of other countries. For this, measurement data were collected from 361 different publicly accessible locations in Dhaka City applying a convenience sampling approach. The measured average electric field exceeded the 1800 MHz threshold values of 36.84, 33.5, and 7.5% of the time compared with the thresholds of China, India, and Japan, respectively, followed by the measured average electromagnetic field values, which were 57, 52, and 29%, respectively. No exceedance was seen for radiofrequency power flux for the investigated countries. Approximately 35% of the calculated average specific energy absorption rate values exceeded the ICNIRP recommended public exposure limit of 0.08 W/kg. Based on this data, it is suggested that detailed NIR exposure regulations need to be created and proper oversight and enforcement over operators are required to avoid potential health effects.

Off-flavor profiling of cultured salmonids using hyperspectral imaging combined with machine learning

Off-flavors can have significant impacts on the quality of salmonid products. This study investigated the possibility of comprehensive off-flavor profiling considering both olfactory and taste sensory perspectives by combining near-infrared hyperspectral imaging (NIR-HSI) and machine/deep learning. Four feature extraction algorithms were employed for the extraction and interpretation of spectral fingerprint information regarding off-flavor-related compounds. Classification models, including the partial least squares discriminant analysis, least-squares support vector machine, extreme learning machine, and one-dimensional convolutional neural network (1DCNN) were constructed using the full wavelengths and selected spectral features for the identification of off-flavor salmonids. The 1DCNN achieved the highest discrimination accuracy with full and selected wavelengths (i.e., 91.11 and 86.39 %, respectively). Furthermore, the prediction and visualization of off-flavor-related compounds were achieved with acceptable performances (R² > 0.6) for practical applications. These results indicate the potential of NIR-HSI for the off-flavor profiling of salmonid muscle samples for producers and researchers.

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.

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.

Using a global diversity panel of Cannabis sativa L. to develop a near InfraRed-based chemometric application for cannabinoid quantification

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.

A Colorimetric Method for the Rapid Estimation of the Total Cannabinoid Content in Cannabis Samples

A colorimetric method for the estimation of the total content of cannabinoids in cannabis samples is proposed. The assay is based on the reaction of these compounds with the reagent Fast Blue B (FBB), which has been immobilized into polydimethylsiloxane (PDMS). The reaction and detection conditions have been established according to the results obtained for the individual cannabinoids Δ⁹-tetrahydrocannabidiol (THC), cannabidiol (CBD), and cannabinol (CBN), as well as for ethanolic extracts obtained from cannabis samples after ultrasonication. In contact with the extract and under basic conditions, the reagent diffuses from the PDMS device, producing a red-brown solution. The absorbances measured at 500 nm after only 1 min of exposure to the FBB/PDMS composites led to responses proportional to the amounts of the cannabinoids in the reaction media. Those absorbances have been then transformed in total cannabinoid content using CBD as a reference compound. The potential utility of the proposed conditions has been tested by analyzing different cannabis samples. The selectivity towards other plants and drugs has been also evaluated. The present method is proposed as a simple and rapid alternative to chromatographic methods for the estimation of the total content of cannabinoids.

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.

Rapid In Situ Detection of THC and CBD in Cannabis sativa L. by 1064 nm Raman Spectroscopy

The need to find a rapid and worthwhile technique for the in situ detection of the content of delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) in Cannabis sativa L. is an ever-increasing problem in the forensic field. Among all the techniques for the detection of cannabinoids, Raman spectroscopy can be identified as the most cost-effective, fast, noninvasive, and nondestructive. In this study, 42 different samples were analyzed using Raman spectroscopy with 1064 nm excitation wavelength. The use of an IR wavelength laser showed the possibility to clearly identify THC and CBD in fresh samples, without any further processing, knocking out the contribution of the fluorescence generated by visible and near-IR sources. The results allow assigning all the Raman features in THC- and CBD-rich natural samples. The multivariate analysis underlines the high reproducibility of the spectra and the possibility to distinguish immediately the Raman spectra of the two cannabinoid species. Furthermore, the ratio between the Raman bands at 1295/1440 and 1623/1663 cm^–1 is identified as an immediate test parameter to evaluate the THC content in the samples.

Rapid Prediction of Mechanical Properties Based on the Chemical Components of Windmill Palm Fiber

During spinning, the chemical component content of natural fibers has a great influence on the mechanical properties. How to rapidly and accurately measure these properties has become the focus of the industry. In this work, a grey model (GM) for rapid and accurate prediction of the mechanical properties of windmill palm fiber (WPF) was established to explore the effect of chemical component content on the Young’s modulus. The chemical component content of cellulose, hemicellulose, and lignin in WPF was studied using near-infrared (NIR) spectroscopy, and an NIR prediction model was established, with the measured chemical values as the control. The value of R_C and R_CV were more than 0.9, while the values of RMSEC and RMSEP were less than 1, which reflected the excellent accuracy of the NIR model. External validation and a two-tailed t-test were used to evaluate the accuracy of the NIR model prediction results. The GM(1,4) model of WPF chemical components and the Young’s modulus was established. The model indicated that the increase in cellulose and lignin content could promote the increase in the Young’s modulus, while the increase in hemicellulose content inhibited it. The establishment of the two models provides a theoretical basis for evaluating whether WPF can be used in spinning, which is convenient for the selection of spinning fibers in practical application.

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.

Cannabinoid Profile in Cannabis sativa L. Samples by Means of LC-MRM/IDA/EPI Analysis: A New Approach for Cultivar Classification

A new multitarget screening procedure for 36 cannabinoids in 12 Cannabis sativa L. cultivars (hemp) was developed using multiple reaction monitoring (MRM) coupled with an enhanced product ion (EPI) scan in an information-dependent acquisition (IDA) experiment, which can be performed by means of high-performance liquid chromatography-mass spectrometry (HPLC-MS)/MS analysis. The MRM-IDA-EPI experiment was used for the analysis of hemp samples and the identification of the compounds of interest. It was performed through the comparison of EPI spectra with literature data and with the in-house library. The results, processed by multivariate statistical analysis, showed an accurate classification of the 12 C. sativa cultivars, emphasizing the synergic contribution of the new cannabinoids recently discovered and showing how the traditional classification based on a common cannabinoid is limiting.

Analytical Techniques for Phytocannabinoid Profiling of Cannabis and Cannabis-Based Products-A Comprehensive Review

Cannabis is gaining increasing attention due to the high pharmacological potential and updated legislation authorizing multiple uses. The development of time- and cost-efficient analytical methods is of crucial importance for phytocannabinoid profiling. This review aims to capture the versatility of analytical methods for phytocannabinoid profiling of cannabis and cannabis-based products in the past four decades (1980–2021). The thorough overview of more than 220 scientific papers reporting different analytical techniques for phytocannabinoid profiling points out their respective advantages and drawbacks in terms of their complexity, duration, selectivity, sensitivity and robustness for their specific application, along with the most widely used sample preparation strategies. In particular, chromatographic and spectroscopic methods, are presented and discussed. Acquired knowledge of phytocannabinoid profile became extremely relevant and further enhanced chemotaxonomic classification, cultivation set-ups examination, association of medical and adverse health effects with potency and/or interplay of certain phytocannabinoids and other active constituents, quality control (QC), and stability studies, as well as development and harmonization of global quality standards. Further improvement in phytocannabinoid profiling should be focused on untargeted analysis using orthogonal analytical methods, which, joined with cheminformatics approaches for compound identification and MSLs, would lead to the identification of a multitude of new phytocannabinoids.

Morphometric relationships and their contribution to biomass and cannabinoid yield in hybrids of hemp (Cannabis sativa)

The breeding of hybrid cultivars of hemp (Cannabis sativa L.) is not well described, especially the segregation and inheritance of traits that are important for yield. A total of 23 families were produced from genetically diverse parents to investigate the inheritance of morphological traits and their association with biomass accumulation and cannabinoid yield. In addition, a novel classification method for canopy architecture was developed. The strong linear relationship between wet and dry biomass provided an accurate estimate of final dry stripped floral biomass. Of all field and aerial measurements, basal stem diameter was determined to be the single best selection criterion for final dry stripped floral biomass yield. Along with stem diameter, canopy architecture and stem growth predictors described the majority of the explainable variation of biomass yield. Within-family variance for morphological and cannabinoid measurements reflected the heterozygosity of the parents. While selfed populations suffered from inbreeding depression, hybrid development in hemp will require at least one inbred parent to achieve uniform growth and biomass yield. Nevertheless, floral phenology remains a confounding factor in selection because of its underlying influence on biomass production, highlighting the need to understand the genetic basis for flowering time in the breeding of uniform cultivars.

Contents of Cannabinoids in Hemp Varieties Grown in Maryland

Coincident with the cannabis legalization and the increased interest in the medicinal use of the plant, the cannabis marketplace and farming have seen tremendous growth. It is reported that there are more than 2000 cannabis varieties available to customers. However, the data that is available to the growers and breeders regarding the cannabinoid contents of various varieties remains low. Here, a high-performance liquid chromatography (HPLC) method was developed and validated for the simultaneous separation and determination of 11 cannabinoids. A total of 104 hemp bud materials belonging to 20 varieties were collected from farms in the state of Maryland and analyzed with the HPLC method. The contents of the 11 cannabinoids in various varieties were compared and discussed, highlighting the varieties that showed a high yield of cannabinoids and good consistency that are more appropriate for cannabinoid production.

Discriminating different Cannabis sativa L. chemotypes using attenuated total reflectance - infrared (ATR-FTIR) spectroscopy: A proof of concept

An original, innovative, high-throughput method based on attenuated total reflectance - Fourier's transform infrared (ATR-FTIR) spectroscopy has been developed for the proof-of-concept discrimination of fibre-type from drug-type Cannabis sativa L. inflorescences. The cannabis sample is placed on the instrument plate and analysed without any previous sample pretreatment step. In this way, a complete analysis lasts just a few seconds, the time needed to record an ATR-FTIR spectrum. The method was calibrated and cross-validated using data provided by liquid chromatography - tandem mass spectrometry (LC-MS/MS) analysis of the different cannabis samples and carried out the statistical assays for quantitation. During cross-validation, complete agreement was obtained between ATR-FTIR and LC-MS/MS identification of the cannabis chemotype. Moreover, the method has proved to be capable of quantifying with excellent accuracy (75-103 % vs. LC-MS/MS) seven neutral and acidic cannabinoids (THC, THCA, CBD, CBDA, CBG, CBGA, CBN) in inflorescences from different sources. The extreme feasibility and speed of execution make this ATR-FTIR method highly attractive as a proof-of-concept for a possible application to quality controls during pharmaceutical product manufacturing, as well as on-the-street cannabis controls and user counselling.

New perspective for the in-field analysis of cannabis samples using handheld near-infrared spectroscopy: A case study focusing on the determination of Δ9-tetrahydrocannabinol

The aim of the present study was to explore the feasibility of applying near-infrared (NIR) spectroscopy for the quantitative analysis of Δ⁹-tetrahydrocannabinol (THC) in cannabis products using handheld devices. A preliminary study was conducted on different physical forms (entire, ground and sieved) of cannabis inflorescences in order to evaluate the impact of sample homogeneity on THC content predictions. Since entire cannabis inflorescences represent the most common types of samples found in both the pharmaceutical and illicit markets, they have been considered priority analytical targets. Two handheld NIR spectrophotometers (a low-cost device and a mid-cost device) were used to perform the analyses and their predictive performance was compared. Six partial least square (PLS) models based on reference data obtained by UHPLC-UV were built. The importance of the technical features of the spectrophotometer for quantitative applications was highlighted. The mid-cost system outperformed the low-cost system in terms of predictive performance, especially when analyzing entire cannabis inflorescences. In contrast, for the more homogeneous forms, the results were comparable. The mid-cost system was selected as the best-suited spectrophotometer for this application. The number of cannabis inflorescence samples was augmented with new real samples, and a chemometric model based on machine learning ensemble algorithms was developed to predict the concentration of THC in those samples. Good predictive performance was obtained with a root mean squared error of prediction of 1.75 % (w/w). The Bland-Altman method was then used to compare the NIR predictions to the quantitative results obtained by UHPLC-UV and to evaluate the degree of accordance between the two analytical techniques. Each result fell within the established limits of agreement, demonstrating the feasibility of this chemometric model for analytical purposes. Finally, resin samples were investigated by both NIR devices. Two PLS models were built by using a sample set of 45 samples. When the analytical performances were compared, the mid-cost spectrophotometer significantly outperformed the low-cost device for prediction accuracy and reproducibility.

Mid-infrared spectroscopy as process analytical technology tool for estimation of THC and CBD content in Cannabis flowers and extracts

Tetrahydrocannabinol (THC) and cannabidiol (CBD) are the most notable Cannabis components with pharmacological activity and their content in the plant flowers and extracts are considered as critical quality parameters. The new Medical Cannabis industry needs to adopt the quality standards of the pharmaceutical industry, however, the variability of phytocannabinoids content in the plant material often exerts an issue in the inconsistency of the finished product quality parameters. Sampling problems and sample representativeness is a major limitation in the end-point testing, particularly when the expected variation of the product quality parameters is high. Therefore, there is an obvious need for the introduction of Process Analytical Technology (PAT) for continuous monitoring of the critical quality parameters throughout the production processes. Infrared spectroscopy is a promising analytical technique that is consistent with the PAT requirements and its implementation depends on the advances in instrumentation and chemometrics that will facilitate the qualitative and quantitative aspects of the technique. Our present work aims in highlighting the potential of mid-infrared (MIR) spectroscopy as PAT in the quantification of the main phytocannabinoids (THC and CBD), considered as critical quality/material parameters in the production of Cannabis plant and extract. A detailed assignment of the bands related to the molecules of interest (THC, CBD) was performed, the spectral features of the decarboxylation of native flowers were identified, and the specified bands for the acid forms (THCA, CBDA) were assigned and thoroughly explained. Further, multivariate models were constructed for the prediction of both THC and CBD content in extract and flower samples from various origins, and their prediction ability was tested on a separate sample set. Savitskzy-Golay smoothing and the second derivative of the native MIR spectra (1800-400 cm^-1 region) resulted in best-fit parameters. The PLS models presented satisfactory R2Y and RMSEP of 0.95 and 3.79% for THC, 0.99 and 1.44% for CBD in the Cannabis extract samples, respectively. Similar statistical indicators were noted for the Partial least-squares (PLS) models for THC and CBD prediction of decarboxylated Cannabis flowers (R2Y and RMSEP were 0.99 and 2.32% for THC, 0.99 and 1.33% for CBD respectively). The VIP plots of all models demonstrated that the THC and CBD distinctive band regions bared the highest importance for predicting the content of the molecules of interest in the respected PLS models. The complexity of the sample (plant tissue or plant extract), the variability of the samples regarding their origin and horticultural maturity, as well as the non-uniformity of the plant material and the flower-ATR crystal contact (in the case of Cannabis flowers) were governing the accuracy descriptors. Taking into account the presented results, ATR-MIR should be considered as a promising PAT tool for THC and CBD content estimation, in terms of critical material and quality parameters for Cannabis flowers and extracts.

Biological Activity of Cannabis sativa L. Extracts Critically Depends on Solvent Polarity and Decarboxylation

Minor cannabinoid and non-cannabinoid molecules have been proposed to significantly contribute to the pharmacological profile of cannabis extracts. Phytoplant Research has developed highly productive cannabis cultivars with defined chemotypes, as well as proprietary methods for the extraction and purification of cannabinoids. Here, we investigate the effect of solvent selection and decarboxylation on the composition and pharmacological activity of cannabis extracts. A library of forty cannabis extracts was generated from ten different cannabis cultivars registered by Phytoplant Research at the EU Community Plant Variety Office. Plant material was extracted using two different solvents, ethanol and hexane, and crude extracts were subsequently decarboxylated or not. Cannabinoid content in the resulting extracts was quantified, and biological activity was screened in vitro at three molecular targets involved in hypoxia and inflammation (NF-κB, HIF-1α and STAT3). Changes in transcriptional activation were strongly associated to solvent selection and decarboxylation. Two decarboxylated extracts prepared with hexane were the most potent at inhibiting NF-κB transcription, while HIF-1α activation was preferentially inhibited by ethanolic extracts, and decarboxylated extracts were generally more potent at inhibiting STAT3 induction. Our results indicate that solvent selection and proper decarboxylation represent key aspects of the standardized production of cannabis extracts with reproducible pharmacological activity.

Emerging challenges in the extraction, analysis and bioanalysis of cannabidiol and related compounds

Cannabidiol (CBD) is a bioactive terpenophenolic compound isolated from Cannabis sativa L. It is known to possess several properties of pharmaceutical interest, such as antioxidant, anti-inflammatory, anti-microbial, neuroprotective and anti-convulsant, being it active as a multi-target compound. From a therapeutic point of view, CBD is most commonly used for seizure disorder in children. CBD is present in both medical and fiber-type C. sativa plants, but, unlike Δ⁹-tetrahydrocannabinol (THC), it is a non-psychoactive compound. Non-psychoactive or fiber-type C. sativa (also known as hemp) differs from the medical one, since it contains only low levels of THC and high levels of CBD and related non-psychoactive cannabinoids. In addition to medical Cannabis, which is used for many different therapeutic purposes, a great expansion of the market of hemp plant material and related products has been observed in recent years, due to its usage in many fields, including food, cosmetics and electronic cigarettes liquids (commonly known as e-liquids). In this view, this work is focused on recent advances on sample preparation strategies and analytical methods for the chemical analysis of CBD and related compounds in both C. sativa plant material, its derived products and biological samples. Since sample preparation is considered to be a crucial step in the development of reliable analytical methods for the determination of natural compounds in complex matrices, different extraction methods are discussed. As regards the analysis of CBD and related compounds, the application of both separation and non-separation methods is discussed in detail. The advantages, disadvantages and applicability of the different methodologies currently available are evaluated. The scientific interest in the development of portable devices for the reliable analysis of CBD in vegetable and biological samples is also highlighted.

Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study

Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A faster and cheaper methodology based on automatic methods can be useful for the detection and identification of Cannabis sativa L. in a reliable and objective manner. In this work, the high potential of Near Infrared Hyperspectral Imaging (HSI-NIR) combined with machine learning is demonstrated for supervised detection and classification of Cannabis sativa L. This plant, together with other plants commonly found in the surroundings of illegal plantations and soil, were directly collected from an illegal plantation. Due to the high correlation of the NIR spectra, sparse Principal Component Analysis (sPCA) was implemented to select the most important wavelengths for identifying Cannabis sativa L. One class Soft Independent Class Analogy model (SIMCA) was built, considering just the spectral variables selected by sPCA. Sensitivity and specificity values of 89.45% and 97.60% were, respectively, obtained for an external validation set subjected to the s-SIMCA. The results proved the reliability of a methodology based on NIR hyperspectral cameras to detect and identify Cannabis sativa L., with only four spectral bands, showing the potential of this methodology to be implemented in low-cost airborne devices.

A Cup of Hemp Coffee by Moka Pot from Southern Italy: An UHPLC-HRMS Investigation

After a long period defined by prohibition of hemp production, this crop has been recently re-evaluated in various industrial sectors. Until now, inflorescences have been considered a processing by-product, not useful for the food industry, and their disposal also represents an economic problem for farmers. The objects of the present work are coffee blends enriched with shredded inflorescences of different cultivars of industrial hemp that underwent solid/liquid extraction into the Italian “moka” coffee maker. The obtained coffee drinks were analyzed by Ultra-High-Performance Liquid Chromatography-High Resolution Mass Spectrometry (UHPLC-HRMS) tools for their quali-quantitative phytocannabinoid profiles. The results showed that they are minor constituents compared to chlorogenic acids and caffeine in all samples. In particular, cannabidiolic acid was the most abundant among phytocannabinoids, followed by tetrahydrocannabinolic acid. Neither Δ⁹-tetrahydrocannabinol (THC) nor cannabinol, its main oxidation product, were detected. The percentage of total THC never exceeded 0.04%, corresponding to 0.4 mg/kg, far below the current maximum limits imposed by the Italian Ministry of Health. This study opens up a new concrete possibility to exploit hemp processing by-products in order to obtain drinks with high added value and paves the way for further in vitro and in vivo investigations aimed at promoting their benefits for human health.

Conversion of cannabidiol (CBD) to Δ9-tetrahydrocannabinol (Δ9-THC) during protein precipitations prior to plasma samples analysis by chromatography - Troubles with reliable CBD quantitation when acidic precipitation agents are applied

The growing popularity of supplements containing cannabidiol (CBD), mainly CBD oils, in self-medication of humans and the increased interest in this compound in different preclinical and clinical trials stimulates the development of procedures of CBD analysis in plasma for the study of CBD pharmacology in people and animals or in establishing dose-therapeutic effect relationships of this compound. Preliminary removal of protein by its precipitation from plasma is still one of the willingly applied plasma sample preparation methods in many analytical procedures estimating plasma drug concentration, including CBD. The present paper shows that a significant amount of CBD transforms to Δ9-tetrahydrocannabinol (Δ9-THC) in a hot GC injection system when acidic precipitation agents, such as TFA, TCA, HClO₄, H₂SO₄, ZnSO₄ or CHCl₃, are used for plasma protein precipitation. The transformation degree depends on the temperature of the GC injector, the concentration of the precipitation agent and the incubation time of plasma with the precipitating agent. At the CBD plasma concentration equal to 50 ng/ml, which is approximately the mean level for patients treated for epileptic syndromes, the CBD transformation degree can exceed 20%. For a reliable estimate of CBD in blood plasma, neutral precipitation agents (e.g. ACN, MeOH, acetone) should be used when plasma deproteinization precedes GC analysis. The presented results are important not only for analysts cooperating with pharmacologists and for medicine doctors examining the activity of CBD-containing drugs in the therapeutic process, but also for forensic scientists who may erroneously find innocent people guilty of using marijuana or its preparations.

Impact of Growth Stage and Biomass Fractions on Cannabinoid Content and Yield of Different Hemp (Cannabis sativa L.) Genotypes

The medicinal use of cannabinoids renewed the interest in industrial hemp (Cannabis sativa L.). The aim of this study was to evaluate the impact of growth stage and biomass fractions of seven industrial hemp genotypes. The study focused on biomass yield, content of cannabidiolic acid/cannabidiol (CBDA/CBD), cannabigerolic acid/cannabigerol (CBGA/CBG), and tetrahydrocannabinolic acid (THCA). The experiment was conducted in 2017 and 2018. The biomass samples were taken at the vegetative (S1), bud (S2), full-flowering (S3) and seed maturity stage (S4). Plants were fractionated into inflorescence, upper and lower leaves. The average inflorescence dry yield of genotypes Futura75, Fédora17, Félina32 and Ferimon ranged between 257.28 g m−2 to 442.00 g m−2, resulting in a maximum yield of CBDA at S4, with 4568.26 mg m−2, 6011.20 mg m−2, 4975.60 mg m−2 and 1929.60 mg m−2, respectively. CBGA was exclusively found in genotype Santhica27, with a maximum CBGA yield of 5721.77 mg m−2 in inflorescence at growth stage S4 and a dry weight yield of 408.99 g m−2. Although these industrial hemp genotypes are mainly cultivated for fibre and seed production, however, cannabinoids offer an additional value. For an optimized harvest result, yield of extractable material and overall yield of cannabinoids must be considered.

MicroNIR/Chemometrics: A new analytical platform for fast and accurate detection of Δ9-Tetrahydrocannabinol (THC) in oral fluids

BACKGROUND

Δ⁹-Tetrahydrocannabinol (THC) is already considered one of the most addictive substances since an increasing number of consumers/abusers of THC and THC based products are observed worldwide. In this work, the capabilities of a novel miniaturized and portable MicroNIR spectrometer were investigated in order to propose a practical and intelligible test allowing the rapid and easy screening of Δ⁹-Tetrahydrocannabinol (THC) oral fluids without any pretreatment.

METHODS

Specimens from volunteers were collected in order to consider any sources of variability in the spectral response and spiked with increasing amount of THC in order to realize predictive models to be used in real cases. Partial Least Square-Discriminant Analysis (PLS-DA) and Partial Least Square regression (PLSr) for the simultaneously detection and quantification of THC, were applied to baseline corrected spectra pre-treated by first derivative transform.

RESULTS

Results demonstrated that MicroNIR/Chemometric platform is statistically able to identify THC abuse in simulated oral fluid samples containing THC from 10 to 100 ng/ml, with a precision and a sensitivity of about 1.51% and 0.1% respectively.

CONCLUSIONS

The coupling MicroNIR/Chemometrics permits to simplify THC abuse monitoring for roadside drug testing or workplace surveillance and provides the rapid interpretation of results, as once the model is assessed, it can be used to process real samples in a "click-on" device.

Construction and Validation of Quantification Methods for Determining the Cannabidiol Content in Liquid Pharma-Grade Formulations by Means of Near-Infrared Spectroscopy and Partial Least Squares Regression

There is an increasing interest in cannabinoids as they are being proved to effectively treat the symptoms of a variety of medical conditions. Commercialization of cannabinoid-based pharmaceutical products is expected to grow in the near future, favored by the recent changes in medical regulations in many developed countries. Hence, robust and reliable analytical methods for determining the content of the active pharmaceutical ingredient will be needed, as this is one of the most relevant parameters for the decision to release the final pharmaceutical product into the market. The aim of this work was to demonstrate that near-infrared (NIR) spectroscopy fulfills the needed requirements for this purpose, as well as to provide a methodology to be applied to other cannabinoid-based products. We present two validated methods for the quantification of different liquid pharma-grade cannabidiol (CBD) formulations based on NIR spectroscopy and partial least squares regression modelling. The methods were constructed and validated with spectra belonging both to production samples and to laboratory samples specifically made for this purpose, and they fulfill European Medicines Agency and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use guideline requirements. These methods allow determining the CBD content with results comparable to the usual method of choice while saving reagent- as well as time-related costs.