No abstract

The United Nations Office on Drugs and Crime has designated several “legal highs” as “plants of concern” because of the dangers associated with their increasing recreational abuse. Routine identification of these products is hampered by the difficulty in distinguishing them from innocuous plant materials such as foods, herbs, and spices. It is demonstrated here that several of these products have unique but consistent headspace chemical profiles and that multivariate statistical analysis processing of their chemical signatures can be used to accurately identify the species of plants from which the materials are derived. For this study, the headspace volatiles of several species were analyzed by direct analysis in real-time high-resolution mass spectrometry (DART-HRMS). These species include Althaea officinalis, Calea zacatechichi, Cannabis indica, Cannabis sativa, Echinopsis pachanoi, Lactuca virosa, Leonotis leonurus, Mimosa hositlis, Mitragyna speciosa, Ocimum basilicum, Origanum vulgare, Piper methysticum, Salvia divinorum, Turnera diffusa, and Voacanga africana. The results of the DART-HRMS analysis revealed intraspecies similarities and interspecies differences. Exploratory statistical analysis of the data using principal component analysis and global t-distributed stochastic neighbor embedding showed clustering of like species and separation of different species. This led to the use of supervised random forest (RF), which resulted in a model with 99% accuracy. A conformal predictor based on the RF classifier was created and proved to be valid for a significance level of 8% with an efficiency of 0.1, an observed fuzziness of 0, and an error rate of 0. The variables used for the statistical analysis processing were ranked in terms of the ability to enable clustering and discrimination between species using principal component analysis–variable importance of projection scores and RF variable importance indices. The variables that ranked the highest were then identified as m/z values consistent with molecules previously identified in plant material. This technique therefore shows proof-of-concept for the creation of a database for the detection and identification of plant-based legal highs through headspace analysis.

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.

Chromatographic-less mass spectrometry techniques like direct analysis in real time mass spectrometry (DART-MS) are steadily being employed as seized drug screening tools. However, these newer analytical platforms require new computational methods to best make-use of the collected data. The inverted library search algorithm (ILSA) is a recently developed method designed specifically for working with mass spectra of mixtures collected with DART-MS, and has been implemented as a function in the NIST/NIJ DART-MS Data Interpretation Tool (DIT). This paper demonstrates how DART-MS and the ILSA/DIT can be used to analyze seized drug evidence, while discussing insights gathered during the evaluation of several adjudicated case samples. The evaluation verified that the combination of DART-MS and the ILSA/DIT can be used as an informative tool to help analysts screen seized drug evidence, but also revealed several factors an analyst must consider while employing these methods—all of these considerations are summarized in this paper.