Development of Ambient Ionization Mass Spectrometric and Multivariate Statistical Analysis Methods for Rapid High Throughput Analysis and Identification of Psychotropic Plant Species

In 2014, the United Nations Office on Drugs and Crime (UNODC) reported that the number of new psychoactive substances including synthetic as well as plant-based products on the global market more than doubled over the period 2009 – 2013. These abused substances include prescription drugs, synthetics such as cannabinoids and cathinones, and whole plant products. Law enforcement efforts to address the manufacture, sale and use of these substances, such as chemical precursor control measures and the scheduling of synthetic psychotropics, have had positive albeit modest impacts on the problem. However, these approaches have had little effect on the increasing use of plant-based psychotropics, such as the 20 species identified by the UNODC as “drugs of concern (DOC).” This is because: (1) the plants are readily available through internet commerce; (2) many of the plants have alternative uses, such as in landscaping as ornamentals; (3) the plant foliage is not generally recognized and therefore it is challenging for law enforcement to visually identify the plants in a forensic context; (4) standard well-established analytical methods (such as GC-MS and LC-MS) that are useful in the identification of purified abused substances are time consuming to perform on whole plant material and/or have not been developed for analysis of whole plant products; (5) it is impractical to develop standard protocols for the large number of plants of abuse; and (6) there is generally no statistical reporting of the level of certainty of a positive identification of a particular plant drug. This combination of factors makes it challenging to legislate the manufacture, sale and abuse of plant drugs. We have proposed that these challenges can be overcome through the application of high throughput ambient ionization mass spectrometric methods such as direct analysis in real time mass spectrometry (DART-MS) for the analysis of whole plant material. The processing of the acquired data by multivariate statistical methods exploits the uniqueness of the characteristic mass spectral fingerprint of each plant species to yield statistical levels of probability of the plants’ identity. Furthermore, this provides the opportunity for the creation of a database of abused plants that can be used to rapidly identify plant products in a forensic scene context. We demonstrate the proof of principle with several of the UNODC-identified DOCs, including Kratom, Datura spp., Salvia spp., and Kava among other plants. This project contains a research and/or development component, as defined in applicable law. ca/ncf