Cannabis Samples Research Articles

Leveraging genetics to support forensic toxicology analysis: Demonstrating concordance among marijuana samples

Cannabis sativa is an important plant for industrial purposes. Indeed, it is legal to cultivate and supply authorized low level Δ9-tetrahydrocannabinol (THC) cannabis plants for fiber and seeds (i.e., 0.2 % according to the European Union regulation on drugs). Today, chromatography/mass spectrometry-based procedures are successfully applied to determine THC concentration. Unfortunately, when cannabis samples have been degraded because wrongfully stored, toxicological results were affected, altering the final status of legal sample or illegal sample. Rather, genetic identification could give more information about the identity of these plants. For this reason, a degradation study was run to support how genetics can help to detect concordance in cannabis highly-degraded samples. Forty cannabis sub-samples were stored (for one week, two weeks, one month and two months) in auto-sealing bag to assess the effects of a wrong cannabis storage on weight reduction, genomic DNA changes, and corresponding STR profiles. Once respective time-period elapsed, each sub-sample’s weight was measured, the percentage weight loss calculated, and the genomic DNA was extracted and quantified, obtaining a complete STR profile for all samples. Furthermore, the aim of this study was to assess the same 13-loci short tandem repeat (STR) multiplex system used for the degradation experiment and evaluate the power of such markers in the identification and discrimination of highly degraded cannabis samples coming from real cases. The results of this study demonstrated that the 13-loci STR multiplex system successfully achieved the objective both for industry and forensic purposes. For each sample, all 13 loci were amplified, and degraded samples were correctly identified, suggesting that genetic typification could be a useful tool. The proposed procedure could be parallelly applied to toxicology analysis to detect if vegetable sample become from authorized plant, to help courts track back illegal samples or to achieve illegal cannabis genetic profiles for further comparison.

A 10-year trend in cannabis potency (2013-2022) in different geographical regions of the United States of America.

The prevalence of cannabis as the most commonly used illicit substance in the United States and around the globe is well-documented. Studies have highlighted a noticeable uptrend in the potency of cannabis in the United states. This report examines the concentration of cannabinoids in illicit cannabis samples seized by the United States Drug Enforcement Administration (DEA) over the last 10 years (2013-2022). Samples received during the course of study (2013-2022) were categorized based on the geographical region where collected, as Western Region, Midwest Region, Northeast Region, South East Region, Southern Region as well as Alaska and Hawaii. These samples were processed for analysis using a validated gas chromatography with flame ionization detector method. The data showed that the cannabinoids profile of all high Δ9-THC cannabis samples, regardless of the state or region from which the samples are seized or the state from which the sample is produced under a state medical marijuana program, is basically the same with the major cannabinoid being Δ9-THC (>10% for most samples) and all other cannabinoids with less than 0.5%, with the exception of CBG (<1%) and CBN (<1%). Overall, it appears the cannabinoids profile is controlled by the genetics of the plant and is not affected much by the geographical location in which the plants are cultivated.

Exploring Genetic Diversity for High CBD Content in Cannabis Accessions in Tropical and Subtropical Regions of India.

Cannabis, also known as marijuana or hemp, has been utilized since ancient times for industrial, religious, recreational, and medical uses.However, regardless of the intended use, there are legal requirements for quantitative testing of cannabinoids across the supply chains. This investigation aimed to evaluate the genetic diversity of 54 Cannabis samples collected from tropical and subtropical regions in India. The research found a high genotypiccoefficient of variation(GCV), phenotypiccoefficient of variation(PCV), heritability, and genetic advance for total cannabidiol (CBD) content. The genotypic and phenotypic correlation among the morpho-chemical characters revealed strong positive correlations among most characters. Clustering and Principal component analysis identified three accessions in cluster II (CIM-CS-65, CIM-CS-189 & CIM-CS-64) with high CBD content that could be used for breeding and as sources of high CBD content in Cannabis. CIM-CS-64, with its high CBD content with 0.01%THC content, holds potential as a valuable parental line for utilization in hybridization programs and recombinant breeding. Furthermore, in accordance with the NDPC Act of 1985, CIM-CS-64 can be commercialized for medicinal purposes, making it a promising source for the development of medicinal CBD products.

Identification of Marijuana Using Silver-Phosphine Ion Complexation and a Semi-Quantitative 1% Decision-Point Assay

Due to the strict federal regulations concerning ∆9-tetrahydrocannabinol (∆9-THC) content for the differentiation of hemp and marijuana outlined in the 2018 Farm Bill, the ability to quickly and reliably differentiate cannabis as marijuana or hemp is crucial within both the seized drug community and hemp industry. This study provides a novel direct mass spectrometry approach for the identification of marijuana using Ag-phosphine ion complexation and a semi-quantitative 1% decision-point assay. The main constituents of hemp and marijuana, cannabidiol (CBD) and ∆9-THC, are isomeric and cannot be differentiated using soft ionization mass spectrometry techniques alone. However, the incorporation of [Ag(PPh3)(OTf)]2 enables the formation of unique MS/MS product ions at m/z 421/423, m/z 353/355, and m/z 231 for CBD due to differences in binding affinity, allowing CBD to be differentiated from ∆9-THC. Likewise, the isomeric cannabinoid precursors ∆9-tetrahydrocannabinolic acid (∆9-THCA) and cannabidiolic acid (CBDA) can be differentiated due to the formation of unique MS/MS product ions at m/z 465/467 and m/z 379/381, which are specific to CBDA. Eight additional cannabinoids were also characterized utilizing the proposed Ag-phosphine ion complexation approach. To reduce the potential for false positives, a more conservative 1% decision-point assay was developed by fortifying 1% weight-by-volume ∆9-THC-d9 into methanolic extracts of authentic cannabis plant material, followed by assessing if the total ∆9-THC content (containing ∆9-THC and ∆9-THCA) was greater than or less than the intensity of the spiked internal standard. When the developed approach was applied to 20 methanolic extracts of authentic cannabis samples with known cannabinoid compositions, 90% of the samples were correctly identified as marijuana or not marijuana based on the 1% administrative threshold for the total ∆9-THC content. The two lone misidentifications were due to the presence of elevated ∆8-THC, which highlights the necessity to explore more selective ligands in the future. This study provides the first application of Ag-ligand ion complexation for the identification of marijuana based on a semi-quantitative 1% decision-point assay, which shows great promise as an alternative method for the rapid identification of marijuana.

Development of an accelerated ageing protocol for the study of phytocannabinoid stability in Cannabis sativa L.

Cannabis sativa L. is a plant belonging to the Cannabaceae family known primarily for its recreational use due to the psychoactive properties of Δ9-tetrahydrocannabinol (THC). Despite this, several compounds belonging to the category of phytocannabinoids have shown in recent years a number of potentially promising therapeutic effects that have increased the interest in the pharmaceutical field towards this plant. However, the content of these compounds is very variable and influenced by different factors, such as growing conditions and time of the year. An indication of the status and age of Cannabis samples is provided by the content of CBN, a minor phytocannabinoid and degradation product of other phytocannabinoids, including THC. In this research work an innovative, solid state analytical approach has been developed to observe and evaluate the variations in the content of two phytocannabinoids (CBN and CBD) in Cannabis-derived products over time. In order to simulate the ageing of the Cannabis samples, an artificially accelerated ageing procedure has been developed and optimised by using high temperatures. The analyses were carried out using an innovative ATR-FTIR method for solid state analysis, enabling direct analysis of a solid sample without any pretreatment phase. This study has allowed the development of an innovative analytical approach for the evaluation of the age and state of conservation of Cannabis samples and may be a useful tool both in the industrial, pharmaceutical and forensic fields.

An initial exploration of machine learning for establishing associations between genetic markers and THC levels in Cannabis sativa samples

Cannabis sativa, a globally commercialized plant used for medicinal, food, fiber production, and recreation, necessitates effective identification to distinguish legal and illegal varieties in forensic contexts. This research utilizes multivariate statistical models and Machine Learning approaches to establish correlations between specific genotypes and tetrahydrocannabinol (Δ9-THC) content (%) in C. sativa samples. 132 cannabis leaves samples were obtained from legal growers in Piedmont, Italy, and illegal drug seizures in Turin. Samples were genetically profiled using a 13-loci STR multiplex and their Δ9-THC content was detected through quantitative GC-MS analysis. This study aims to assess the use of supervised classification modelling on genetic data to distinguish cannabis samples into legal and illegal categories, revealing distinct clusters characterized by unique allele profiles and THC content. t-distributed Stochastic Neighbor Embedding (t-SNE), Random Forest (RF) and Partial Least Squares Regression (PLS-R) were executed for the machine learning modelling. All the tested models resulted effective discriminating between legal samples and illegal. Although further validation is necessary, this study presents a novel forensic investigative approach, potentially aiding law enforcement in significant marijuana seizures or tracking illicit drug trafficking routes.

Rapid In Situ Near-Infrared Assessment of Tetrahydrocannabinolic Acid in Cannabis Inflorescences before Harvest Using Machine Learning.

Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects. As the global cannabis industry and research into cannabinoids expands, more efficient and cost-effective analysis methods for determining cannabinoid concentrations will be beneficial to increase efficiencies and maximize productivity. The utilization of machine learning tools to develop near-infrared (NIR) spectroscopy-based prediction models, which have been validated from accurate and sensitive chemical analysis, such as gas chromatography (GC) or liquid chromatography mass spectroscopy (LCMS), is essential. Previous research on cannabinoid prediction models targeted decarboxylated cannabinoids, such as THC, rather than the naturally occurring precursor, tetrahydrocannabinolic acid (THCA), and utilize finely ground cannabis inflorescence. The current study focuses on building prediction models for THCA concentrations in whole cannabis inflorescences prior to harvest, by employing non-destructive screening techniques so cultivators may rapidly characterize high-performing cultivars for chemotype in real time, thus facilitating targeted optimization of crossbreeding efforts. Using NIR spectroscopy and LCMS to create prediction models we can differentiate between high-THCA and even ratio classes with 100% prediction accuracy. We have also developed prediction models for THCA concentration with a R2 = 0.78 with a prediction error average of 13%. This study demonstrates the viability of a portable handheld NIR device to predict THCA concentrations on whole cannabis samples before harvest, allowing the evaluation of cannabinoid profiles to be made earlier, therefore increasing high-throughput and rapid capabilities.

Comprehensive two-dimensional gas chromatography using a miniaturized multiloop splitter-based non-cryogenic artificial trapping (M-SNAT) modulation device for analysis of cannabis samples

Multiloop splitter-based non-cryogenic artificial trapping (M-SNAT) modulation technique was developed, miniaturized and applied in comprehensive two-dimensional gas chromatography (GC×GC) for analysis of cannabis samples. The approach employed deactivated fuse silica (DFS) columns configured into multiple loop splitter system halving the perimeters of the progressively upstream loops. This splitter device was located between the first (1D) semi-nonpolar column outlet and a microfluidic Deans switch (DS). Each splitter loop splits a peak into two subpeaks having the same area with different void times. Three loops were then applied resulting in the number of the split subpeaks (nsplit) of 8 for each peak, and retention time differences between any two adjacent subpeaks (∆tR,split) were the same. By applying periodic heartcut event (H/C) within every artificial modulation period (PAM) of nsplit×∆tR,split, comprehensive split-and-trapped modulation profiles of analytes could be selectively transferred onto the second (2D) polar column (30 m) without cryogen consumption. This artificial modulation system was applied for analysis of cannabis samples with enhanced 2D peak capacity (2nc∼15). The established method was applied to analyse cannabis extracts using vegetable oils with or without frying process. This reveals 454 different peaks with 76, 92, 35 and 70 specific components specifically observed by using olive oil extraction (OE), fried OE, coconut oil extraction (CE) and fried CE, respectively.

Microwave-assisted hot air drying of Cannabis sativa: Effect of vacuum and pre-freezing on drying kinetics and quality

Drying is an important postharvest step preceding long term storage of medicinal plants, and optimized drying conditions are critical for secondary metabolite preservation. The objective of this research was to determine the effect of pre-freezing, vacuum, drying temperature, and microwave power on cannabis, including drying kinetics, color retention, and moisture diffusivity, and cannabinoid and terpene concentrations. Harvested inflorescence was dried under four drying conditions, namely untreated, dried without vacuum, untreated, dried with vacuum, pre-frozen, dried without vacuum, and pre-frozen, dried with vacuum. The untreated groups were dried and analysed immediately after harvest and the pre-frozen group was frozen at −20°C for 24 h prior to drying and analysis. Results showed that moisture diffusion was significantly affected by the temperature, vacuum and microwave power and reduced exponentially with time. For untreated samples dried at a microwave power of 2 W g−1, it took only 61 min (with no vacuum) and 57.3 min (under vacuum) at 65°C to reach the desired final moisture content of 11 % dry basis. Increasing drying temperature from 35°C to 50°C at 1 W g−1 under vacuum reduced the drying time by 60.1 % and 68 % for pre-frozen and untreated samples, respectively and reduced by 68 % and 65.5 % for pre-frozen and untreated cannabis samples, respectively for samples dried without vacuum. Pre-freezing prior to hot air drying with and without vacuum can be used to preserve the color of biomass and cannabinoids. The effect was prominent in the acidic cannabinoids where pre-freezing prior to hot air drying at 50°C without vacuum increased tetrahydrocannabinolic acid and cannabigerolic acid concentrations from 20.6 to 23.6 g 100 g dry matter−1 and 0.47–0.61 g 100 g dry matter−1. Results presented add important knowledge for the optimization of postharvest steps for cannabis.

Differentiation of Δ9-THC and CBD Using Silver-Ligand Ion Complexation and Electrospray Ionization Tandem Mass Spectrometry (ESI-MS/MS).

The 2018 Farm Bill defines marijuana as Cannabis sativa L. or any derivative thereof that contains greater than 0.3% Δ9-tetrahydrocannabinol (Δ9-THC) on a dry weight basis. The main cannabinoids present in Cannabis sativa L., Δ9-THC and cannabidiol (CBD), are structural isomers that cannot be differentiated using direct mass spectrometry with soft ionization techniques alone. Due to the classification of marijuana as a Schedule I controlled substance, the differentiation of Δ9-THC and CBD is crucial within the seized drug community. This study explores the use of Ag-ligand ion complexation and electrospray ionization tandem mass spectrometry (ESI-MS/MS) for the differentiation of Δ9-THC and CBD using six different Ag complexes. Differences between the binding affinities of Δ9-THC and CBD for [Ag(PPh3)(OTf)]2 lead to the formation of unique product ions at m/z 421/423, m/z 353/355, and m/z 231 for CBD, enabling the differentiation of CBD from Δ9-THC. When applied to the analysis of known Δ9-THC:CBD mixture ratios, the developed [Ag(PPh3)(OTf)]2 ion complexation method was able to differentiate Δ9-THC-rich and CBD-rich samples based on the average abundance of the product ions at m/z 421/423. The developed approach was then applied to methanolic extracts of 20 authentic cannabis samples with known Δ9-THC and CBD compositions, resulting in a 95% correct classification rate. Even though the developed Ag-ligand ion complexation method was only demonstrated for the qualitative differentiation of Δ9-THC-rich and CBD-rich cannabis, this study establishes a foundation for the use of Ag-ligand ion complexation that is essential for future quantitative approaches.

Sensor for Rapid In-Field Classification of Cannabis Samples Based on Near-Infrared Spectroscopy.

A rugged handheld sensor for rapid in-field classification of cannabis samples based on their THC content using ultra-compact near-infrared spectrometer technology is presented. The device is designed for use by the Austrian authorities to discriminate between legal and illegal cannabis samples directly at the place of intervention. Hence, the sensor allows direct measurement through commonly encountered transparent plastic packaging made from polypropylene or polyethylene without any sample preparation. The measurement time is below 20 s. Measured spectral data are evaluated using partial least squares discriminant analysis directly on the device's hardware, eliminating the need for internet connectivity for cloud computing. The classification result is visually indicated directly on the sensor via a colored LED. Validation of the sensor is performed on an independent data set acquired by non-expert users after a short introduction. Despite the challenging setting, the achieved classification accuracy is higher than 80%. Therefore, the handheld sensor has the potential to reduce the number of unnecessarily confiscated legal cannabis samples, which would lead to significant monetary savings for the authorities.

ATR-FTIR spectroscopy and LDA: a rapid, non-destructive and cost effective strategy to trace the geographical origin of Cannabis sativa L

Abstract Cannabis recreational and/or medicinal use have been legalized in the past years in many states and countries. As a consequence, many Cannabis growers and product developers have emerged in a new market throughout the world; at the same time, issues regarding questionable quality control have also risen, as several reports on Cannabis users’ health-related problems caused by inaccurate labeling content in Cannabis-based medicines, edibles or other derivatives are being published and brought out to the public’s attention. These facts make traceability methodologies crucial whether for forensic use, such as drug trafficking eradication, or for quality control purposes of legal Cannabis and of products derived from it. Hence, the objective of this study was to analyze Cannabis by means of Attenuated Total Reflection Spectroscopy (ATR-FTIR) in order to assess the capability of this technique to trace the geographical origin of Cannabis cultivated in Brazil and in Colorado, United States of America. Forty-seven samples from Brazil and 18 samples from Colorado were analyzed by ATR-FTIR. Linear Discriminant Analysis (LDA) was employed to source the samples. The combination of ATR-FTIR and LDA achieved up to 95.23% accuracy in assigning Cannabis samples to their geographical locations of origin in Brazil and up to 100% in Colorado.

Identification of Aspergillus westerdijkiae and its potential risk of Ochratoxin A synthesis in Cannabis inflorescences

Nowadays, fungal contamination of medical Cannabis inflorescences during postharvest has become an increasingly frequent and worrisome problem for consumers and the industry in general. This is because some of these microorganisms can produce secondary metabolites, such as mycotoxins, which can be toxic to humans. To assess the risk posed by fungal contamination and evaluate the potential for fungal isolates to produce mycotoxins, samples of medicinal Cannabis were tested for the presence of mycotoxin-forming fungi. Inflorescences were isolated on PDA agar at 23 ± 2 °C for ten days, and the microorganisms were identified. The strain with morphological characteristics compatible with the genus Aspergillus spp. was selected as the fungus with the highest risk of forming hazardous mycotoxins. This isolate was characterized conventionally and by molecular identification using primers for the internal transcribed spacer region (ITS) of ribosomal DNA and different coding genes and was identified as Aspergillus westerdijkiae. To determine mycotoxin formation, the genome of A. westerdijkiae was sequenced using the Illumina Novaseq platform in South Korea. The antiSMASH tool was used to search for gene clusters associated with producing secondary metabolites, and genes related to toxins were manually curated. Regions where the cluster of genes directly involved in OTA biosynthesis (otaA, otaB, otaC, otaR and otaD) were found, suggesting a potential risk of synthesis of this toxin.

Determination of Δ9-THC, THCA, Δ8-THC, and total Δ9-THC in 53 smokable hemp plant products by liquid chromatography and photodiode array detection

The passage of the 2018 Farm Bill defined hemp as cannabis plant containing 0.3 % or less of Δ9-THC, which led to a large increase in hemp production in the United States in 2021. Approximately 76 % of it focused on floral hemp that is used to produce hemp-derived finished products such as smokable hemp (e.g., manicured, roll your own, or cigarettes). As a result, forensic laboratories have seen a significant increase in confiscated cannabis samples, but few reliable analytical methods exist for differentiation between hemp and marijuana. In response to the need for reliable quantitative methods, the National Institute of Standards and Technology (NIST) has developed and evaluated analytical methods to provide forensic scientists the tools necessary. In this manuscript, 53 smokable hemp plant products were analyzed for Δ8-THC, Δ9-THC, THCA, and total Δ9-THC by a previously established liquid chromatography with photodiode array detection (LC-PDA) method using a methanol extraction procedure. Over 90 % of the samples analyzed by NIST were determined to have a total Δ9-THC mass fraction above 0.3 % even though samples were being marketed as hemp. Surprisingly, often the associated online documentation reported total Δ9-THC mass fractions of ≥ 0.3 %. Mass fractions determined by NIST were compared with manufacturer’s online documentation for 22 samples. Measurements differed by ≈ 55 % for total Δ9-THC, ≈ 68 % for THCA, and ≈ 18 % for Δ9-THC. Poor agreement may result from method difference, sample inhomogeneity, batch to batch variability, changes due to storage conditions, and/or product labels or online documentation that are not representative of actual products.

Supplemental Material for Reward Learning Capacity in a Community Sample of Individuals Who Use Cannabis

Supplemental Material for Reward Learning Capacity in a Community Sample of Individuals Who Use Cannabis

Enhanced cannabis timeline followback (EC-TLFB): Comprehensive assessment of cannabis use including standard THC units and validation through biological measures.

The aims of this study were to present an enhanced cannabis timeline followback (EC-TLFB) enabling comprehensive assessment of cannabis use measures, including standard tetrahydrocannabinol (THC) units, and to validate these against objectively indexed urinary 11-nor-9-carboxy-tetrahydrocannabinol (THC-COOH) concentrations. We used cross-sectional baseline data from the 'CannTeen' observational longitudinal study. The study was conducted in London, UK. A total of 147 participants who used cannabis regularly took part in the study (n = 71 female, n = 76 male; mean age = 21.90, standard deviation = 5.32). The EC-TLFB was used to calculate frequency of cannabis use, method of administration, including co-administration with tobacco, amount of cannabis used (measured with unaided self-report and also using pictorial aided self-report) and type of cannabis product (flower, hash) which was used to estimate THC concentration (both from published data on THC concentration of products and analysis of cannabis samples donated by participants in this study). We calculated total weekly standard THC units (i.e. 5 mg THC for all cannabis products and methods of administration) using the EC-TLFB. The outcome variable for validation of past week EC-TLFB assessments was creatinine-normalized carboxy-tetrahydrocannabinol (THC-COOH) in urine. All measures of cannabis exposure included in this analysis were positively correlated with levels of THC-COOH in urine (r = 0.41-0.52). Standard THC units, calculated with average concentrations of THC in cannabis in the UK and unaided self-report measures of amount of cannabis used in grams showed the strongest correlation with THC-COOH in urine (r = 0.52, 95% bias-corrected and accelerated = 0.26-0.70). The enhanced cannabis timeline followback (EC-TLFB) can provide a valid assessment of a comprehensive set of cannabis use measures including standard tetrahydrocannabinol units as well as and traditional TLFB assessments (e.g. frequency of use and grams of cannabis use).

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

Cannabis sativa L. is an ancient cultivar that has found applications in various fields, e.g., medicine, due to its beneficial effects. However, due to its psychotropic effects, the regulation of this cultivar has increased throughout the decades. In this context, the need for rapid and reliable analytical methods to ensure the quality control of Cannabis cultivars has become of extreme importance. NIRS has arisen as a powerful tool in this field due to its multiple advantages, e.g., non-destructive, rapid, and cost-effective. In this article, the chemometric techniques commonly employed in NIRS method development are described, along with their application for the analysis of Cannabis samples. Regarding qualitative methods, different mathematical treatments and classification models are explained. As for quantitative methods, the representative linear and non-linear modelling techniques applied for the development of prediction equations are described, alongside their application in the Cannabis field. To the best of our knowledge, this is the first time this type of review is written, since there are several articles which address cannabinoid determination, but the main purpose of this review is to enhance the potential of NIRS over the traditional techniques employed for the analysis of Cannabis samples.

27 Risky Decision-Making Moderates the Association Between Motives for Cannabis Use and Cannabis Use Trajectories Among Adolescents

Objective:Prior literature has documented how motives for cannabis use predict frequency of use and cannabis use problems among adolescents. However, few studies have examined possible moderating variables that may influence the association between cannabis use motives and frequency of use. The current study examines how risky decision-making moderates this association to help better understand which individuals are at greater risk for cannabis use escalation. The current study will be the first to examine the interactive effects of motives for cannabis use (i.e., health or recreational reasons) and risky decision-making on cannabis use trajectories among a sample of adolescent cannabis users.Participants and Methods:Data from 194 adolescent cannabis users aged 14–17 at baseline were analyzed as part of a larger longitudinal study. Participants included those who self-reported use of cannabis within six months prior to the baseline assessment. The Marijuana Reasons for Use Questionnaire (MJRUQ) was used to assess motives for cannabis use from a list of 13 items. A confirmatory factor analysis identified “health” and “recreational” factors for motives for cannabis use. Lifetime frequency of cannabis use (number of days used) was assessed through the Drug Use History Questionnaire, while risky decision-making was assessed using the Game of Dice Task. We used latent growth curve modeling and linear regression analyses to examine the interactive effects of motives for cannabis use and risky decision-making on initial levels of lifetime cannabis use at baseline, and rate of cannabis use escalation over time.Results:No significant interactive effects were found for health motives for cannabis use; however, we found significant main effects of health motives on initial levels of lifetime cannabis use at baseline (b = 100.82, p &lt; .01) and rate of cannabis use escalation (b = 24.79, p &lt; .01). Those with a greater proclivity to use cannabis for health purposes showed higher initial levels of lifetime use at baseline and steeper increases in the rate of cannabis use escalation relative to those less likely to use for health purposes. Furthermore, we found a significant interactive effect of recreational motives for use and risky decision-making on the rate of cannabis use escalation (b = -2.53, p &lt; .01). Follow-up analyses revealed that among those less likely to use cannabis for recreational purposes, higher risky decision-making was associated with a steeper increase in the rate of cannabis use escalation relative to those who exhibited lower risky decision-making.Conclusions:The current study replicated findings suggesting that cannabis use motives influence cannabis use trajectories. We found that using cannabis primarily for health reasons was associated with higher initial levels and steeper increases in use regardless of decision-making. Furthermore, we found that both motives for use and risky decision-making interacted to influence associations with cannabis use trajectories. Specifically, among individuals reporting less cannabis use for recreational reasons, those with relatively riskier decision-making showed steeper increases in the rate of cannabis use escalation. These findings inform prevention and intervention practices that focus on decision-making by tailoring approaches based on an individual’s primary motives for cannabis use.

Characterization and evaluation of nine Cannabis sativa chloroplast SNP markers for crop type determination and biogeographical origin on European samples

Cannabis sativa can be classified in two main types, according to psychotropic cannabinoid ∆9-tetrahydrocannabinol (∆9-THC) content: the drug-type and the fiber-type. According to the European Monitoring Center for Drugs and Drug Addiction, most of the European Union countries consider the possession of cannabis, for personal use, a minor offense with possibility of incarceration. Despite of the model of legal supply (i.e., Spanish cannabis clubs, Netherlands coffee shops) or medical use (i.e., Italy), cannabis remains the most used and trafficked illicit plant in the European Union. Differentiating cannabis crops or tracing the biogeographical origin is crucial for law enforcement purposes. Chloroplast DNA (cpDNA) markers may assist to determine biogeographic origin and to differentiate hemp from marijuana. This research aims: to identify and to evaluate nine C. sativa cpDNA polymorphic SNP sites to differentiate crop type and to provide information about its biogeographical origin. Five SNaPshot™ assays for nine chloroplast markers were developed and conducted in marijuana samples seized in Chile, the USA-Mexico border and Spain, and hemp samples grown in Spain and in Italy. The SNapShot™ assays were tested on 122 cannabis samples, which included 16 blind samples, and were able to differentiate marijuana crop type from hemp crop type in all samples. Using phylogenetic analysis, genetic differences were observed between marijuana and hemp samples. Moreover, principal component analysis (PCA) supported the relationship among hemp samples, as well as for USA-Mexico border, Spanish, and Chilean marijuana samples. Genetic differences between groups based on the biogeographical origin and their crop type were observed. Increasing the number of genetic markers, including the most recently studied ones, and expanding the sample database will provide more accurate information about crop differentiation and biogeographical origin.

Critical evaluation of analytical methodologies for the discrimination of hemp and drug-type cannabis

Classification of cannabis samples as hemp- or drug-type is an ever-increasing problem worldwide. Colorimetric tests and infrared spectroscopy were selected and critically compared among the available techniques. Cannabis samples with different THC and CBD concentration were analyzed by paper-based analytical devices (µPAD) using the 4-aminophenol colorimetric reaction, attenuated total reflectance mid infrared spectroscopy (ATR-MIR), and diffuse reflectance near infrared spectroscopy (DR-NIR). For the µPAD analysis, cannabis samples were extracted with methanol and later mixed with reagents to develop the colored complex. On the other hand, MIR and NIR spectra were obtained directly on raw samples, and partial least square discriminant analysis (PLS-DA) was used for cannabis classification. All the selected methodologies successfully discriminate between hemp-type and drug-type samples, being suitable to be used in-field. Additionally, assayed methodologies together a reference gas chromatography mass spectrometry (GC–MS) were evaluated and compared from a Green Analytical point of view using the AGREE criteria.