DART-MS Facilitated Quantification of Cannabinoids in Complex Edible Matrices-Focus on Chocolates and Gelatin-Based Fruit Candies.

Traditional methods for detecting and quantifying cannabinoids in Cannabis sativa materials are most often chromatography-based, and they generally require extensive sample preparation protocols to render materials into a form that can be injected into the systems without the risk of contaminating or damaging the equipment. This challenge is amplified when interrogating the increasingly broad range of matrix types that cannabinoids are infused within, such as edibles that also contain sugars, fats, lipids, and carbohydrates. The requisite application of highly nuanced methods that must be developed for each matrix type is, in addition to being resource-intensive and time-consuming, highly impractical and unsustainable for crime laboratories endeavoring to perform such analyses in a routine manner, since they are often under-resourced while typically also confronting sample testing backlogs. A key to resolving this issue is to identify an analysis approach that avoids the requirement for nuanced method development by being applicable to a broader range of matrix types. Ambient ionization mass spectrometry (AIMS) methods have shown great promise in their ability to rapidly interrogate samples. Therefore, this study focused on developing validated protocols using AIMS (specifically, direct analysis in real time-high-resolution mass spectrometry, or DART-HRMS) to detect and quantify Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) in edible matrices. Calibration curves were developed using deuterated counterparts of THC and CBD as internal standards. Following the use of high cannabinoid recovery rate extraction protocols for chocolates and gelatin-based fruit candies or "gummies", the DART-HRMS approach was applied to quantify cannabinoid levels in commercially available cannabinoid-infused candies, yielding results similar to those reported on the product labels. Importantly, the developed method circumvented challenges encountered using traditional approaches. As the Cannabis field continues to evolve and new matrix types emerge on the market, the DART-HRMS detection and quantification protocols can be readily applied without the need for major procedural adaptations.

Combined ambient ionization mass spectrometric and chemometric approach for the differentiation of hemp and marijuana varieties of Cannabis sativa.

BACKGROUND: Hemp and marijuana are the two major varieties of Cannabis sativa. While both contain Δ9-tetrahydrocannabinol (THC), the primary psychoactive component of C. sativa, they differ in the amount of THC that they contain. Presently, U.S. federal laws stipulate that C. sativa containing greater than 0.3% THC is classified as marijuana, while plant material that contains less than or equal to 0.3% THC is hemp. Current methods to determine THC content are chromatography-based, which requires extensive sample preparation to render the materials into extracts suitable for sample injection, for complete separation and differentiation of THC from all other analytes present. This can create problems for forensic laboratories due to the increased workload associated with the need to analyze and quantify THC in all C. sativa materials. METHOD: The work presented herein combines direct analysis in real time-high-resolution mass spectrometry (DART-HRMS) and advanced chemometrics to differentiate hemp and marijuana plant materials. Samples were obtained from several sources (e.g., commercial vendors, DEA-registered suppliers, and the recreational Cannabis market). DART-HRMS enabled the interrogation of plant materials with no sample pretreatment. Advanced multivariate data analysis approaches, including random forest and principal component analysis (PCA), were used to optimally differentiate these two varieties with a high level of accuracy. RESULTS: When PCA was applied to the hemp and marijuana data, distinct clustering that enabled their differentiation was observed. Furthermore, within the marijuana class, subclusters between recreational and DEA-supplied marijuana samples were observed. A separate investigation using the silhouette width index to determine the optimal number of clusters for the marijuana and hemp data revealed this number to be two. Internal validation of the model using random forest demonstrated an accuracy of 98%, while external validation samples were classified with 100% accuracy. DISCUSSION: The results show that the developed approach would significantly aid in the analysis and differentiation of C. sativa plant materials prior to launching painstaking confirmatory testing using chromatography. However, to maintain and/or enhance the accuracy of the prediction model and keep it from becoming outdated, it will be necessary to continue to expand it to include mass spectral data representative of emerging hemp and marijuana strains/cultivars.

Rapid Detection and Quantification of Hallucinogenic Salvinorin A in Commercial Salvia divinorum Products by DART-HRMS.

In recent years, national laboratories have identified several plant-derived materials as concerns to public health because of their psychoactive effects, potential for abuse, and the lack of federal regulation of their use. One of these is Salvia divinorum (aka Salvia), which has received focused attention due to its increasing recreational use and the ease by which it can be acquired. Traditional chromatographic approaches for the detection of the major psychoactive component of Salvia (i.e., salvinorin A) typically require time-consuming sample pretreatment prior to identifying the presence of salvinorin A in plant material unknowns. In this study, direct analysis in real time-high-resolution mass spectrometry (DART-HRMS) was used to rapidly screen for Salvia plant material. This approach facilitated the analysis of bulk material in its native form, thereby bypassing sample pretreatment steps. In addition, a validated DART-HRMS method was developed for the quantification of salvinorin A in commercial Salvia products (e.g., raw plant materials, enhanced leaf extracts). In this regard, cholesterol was found to be a suitable internal standard. The average salvinorin A content in raw Salvia leaves was determined to be 1.54 mg/g, while the salvinorin A quantified in enhanced Salvia leaf extracts was between 13.0 and 53.2 mg/g.

Introducing "DoPP": A Graphical User-Friendly Application for the Rapid Species Identification of Psychoactive Plant Materials and Quantification of Psychoactive Small Molecules Using DART-MS Data.

The widespread abuse of "legal high" psychoactive plants continues to be of global concern because of their negative impacts on public health and safety. In forensic science, a major challenge in controlling these substances is the paucity of methods to rapidly identify them. We report the development of the Database of Psychoactive Plants (DoPP), a new user-friendly tool featuring an architecture for the identification of plant unknowns, and the necessary regression statistics for the development and validation of psychoactive compound quantification. The application relies on the knowledge that terrestrial plants exhibit species-specific chemical signatures that can be revealed by direct analysis in real time─high-resolution mass spectrometry (DART-HRMS). Subsequent automated machine learning processing of libraries of these spectra enables rapid discrimination and species identification. The chemical signature database includes 57 available plant species. The rapid acquisition of mass spectra and the ability to sample the materials in their native form enabled the generation of the vast amounts of spectral replicates required for database construction. For the identification of sample unknowns, a data analysis workflow was developed and implemented using the DoPP tool. It utilizes a hierarchical classification tree that integrates three machine learning methods, namely, random forest, k-nearest neighbors, and support vector machine, all of which were fused using posterior probabilities. The results show accuracies of 98 and 99% for 10-fold cross-validation and external validation, respectively, which make the classification model suitable for identity prediction of real samples.

Detection and Quantification of Psychoactive N,N-Dimethyltryptamine in Ayahuasca Brews by Ambient Ionization High-Resolution Mass Spectrometry.

The United Nations Office on Drugs and Crime designated twenty psychoactive botanical species as "plants of concern" because of their increased recreational abuse. Four of these are used to prepare ayahuasca brews. The complexity of the plant matrices, as well as the beverage itself, make the identification and quantification of the Schedule I component, N,N-dimethyltryptamine (DMT), a time-consuming and resource-intensive endeavor when performed using conventional approaches previously reported. Reported here is the development of a rapid validated method for the quantification of DMT in ayahuasca by direct analysis in real time-high-resolution mass spectrometry (DART-HRMS). This ambient ionization approach also enables identification of ayahuasca through detection of the secondary metabolites associated with its plant constituents. Analysis of six ayahuasca brews created using different combinations of DMT/harmala alkaloid-containing plants resulted in beverages with DMT levels of 45.7-230.5 mg/L. The detected amounts were consistent with previously reported values determined by conventional approaches.

Rapid detection and validated quantification of psychoactive compounds in complex plant matrices by direct analysis in real time-high resolution mass spectrometry - Application to "Kava" psychoactive pepper products.

RATIONALE: Classified by the UNODC as a top 20 plant of concern, Piper methysticum (also known as Kava) is being increasingly abused recreationally for its mind-altering effects. It is of significant forensic relevance to establish methods to rapidly identifyand quantify psychoactive compounds, especially those yet to be scheduled ascontrolled substances and which have exhibited various noteworthy health concerns. METHODS: Direct analysis in real time high-resolution mass spectrometry (DART-HRMS) demonstrated the ability to detect a range of kavalactones in Pipermethysticum derived products and plant material with no sample preparation. Inaddition, a validated method using calibration curves developed with a deuteratedinternal standard was used for the quantification of the psychoactive moleculeyangonin in various products. RESULTS: DART-HRMS detected the protonated masses of six major kavalactonesand three flavokavains in 18 commercial Kava products. A method consistent withFDA validation guidelines was established for the quantification of yangonin in thevarious complex matrices. Implementation of this method, with an LLOQ of 5 mg/mL, enabled successful quantification of yangonin in 16 Kava products.Concentrations for solid products ranged from 2.71 to 8.99 mg/g, while that forliquid products ranged from 1.03 to 4.59 mg/mL. CONCLUSIONS: Rapid identification and quantification of psychoactive smallmolecules in plant material can be accomplished using a validated DART-HRMSprotocol. This work illustrates an approach to qualitative and quantitative analysesof a wide variety of complex matrices derived from plants, and demonstrates thatthe commercially available products analyzed are P. methysticum derived and docontain psychoactive yangonin at quantifiable levels.