Call it a "nightshade"-A hierarchical classification approach to identification of hallucinogenic Solanaceae spp. using DART-HRMS-derived chemical signatures

• NCJ Number: 301228
• Journal: Talanta Volume: 204 Dated: 2019 Pages: 739-746
• Author(s): S. Beyramysoltan; et al
• Date Published: 2019
• Length: 8 pages
• Grant Number(s): 2015-DN-BX-K057
• Agencies: NIJ
• Award: Development of Ambient Ionization Mass Spectrometric and Multivariate Statistical Analysis Methods for Rapid High Throughput Analysis and Identification of Psychotropic Plant Species

Annotation

In this project, ambient ionization mass spectrometry in combination with a hierarchical classification workflow was shown to identify nightshade plant species.

Abstract

Plants that produce atropine and scopolamine fall under several genera within the nightshade family. Both atropine and scopolamine are used clinically, but they are also important in a forensics context because they are abused recreationally for their psychoactive properties. The accurate species attribution of these plants, which are related taxonomically, and which all contain the same characteristic biomarkers, is a challenging problem in both forensics and horticulture, because these plants are not only mind-altering, but are also important in landscaping as ornamentals. The hierarchical classification of the current study simplifies the classification problem to consider primarily the subset of models that account for the hierarchy taxonomy, instead of having it be based on discrimination between species by using a single flat classification model. Accordingly, the seeds of 24 nightshade plant species spanning five genera (i.e. Atropa, Brugmansia, Datura, Hyocyamus and Mandragora), were analyzed by direct analysis in real time-high resolution mass spectrometry (DART-HRMS), with minimal sample preparation required. During the training phase that used a top-down hierarchical classification algorithm, the best set of discriminating features were selected and evaluated with a partial least square-discriminant analysis (PLS-DA) classifier to discriminate and visualize the data. The method yields species identity through a class hierarchy and reveals the most significant markers for differentiation. The overall accuracy of the approach for species identification was 95 percent and 96 percent, using 100X bootstrapping validation and test samples respectively. The method can be extended for the rapid identification of an infinite number of plant species. 1 figure (publisher abstract modified)