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

• NCJ Number: 307819
• Journal: Analytical Chemistry Volume: 94 Issue: 48 Dated: 2022 Pages: 16570–16578
• Author(s): Samira Beyramysoltan; Megan I. Chambers; Amy M. Osborne; Mónica I. Ventura; Rabi A. Musah
• Date Published: 2022
• Length: 9 pages
• Grant Number(s): 2015-DN-BX-K057; 2019-BU-DX-0026
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)
• Award: Development of Ambient Ionization Mass Spectrometric and Multivariate Statistical Analysis Methods for Rapid High Throughput Analysis and Identification of Psychotropic Plant Species

Annotation

This study introduces the Database of Psychoactive Plants (DoPP), a user-friendly tool for the identification, classification, and assessment of psychoactive plant materials.

Abstract

The authors 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 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. In forensic science, a major challenge in controlling these substances is the paucity of methods to rapidly identify them. The DoPP 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. (Published Abstract Provided)