Appley Meghan Grace, Beyramysoltan Samira, Musah Rabi Ann
Department of Chemistry, University at Albany-State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States.
ACS Omega. 2019 Sep 11;4(13):15636-15644. doi: 10.1021/acsomega.9b02145. eCollection 2019 Sep 24.
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 , , , , , , , , , , , , , , and . 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 -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 / 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.
联合国毒品和犯罪问题办公室已将几种“新型毒品”指定为“受关注植物”，因为其娱乐性滥用的增加带来了诸多危险。由于难以将这些产品与食品、草药和香料等无害植物材料区分开来，对它们进行常规识别受到了阻碍。本文证明，其中几种产品具有独特但一致的顶空化学特征，并且对其化学特征进行多变量统计分析处理可用于准确识别这些材料所源自的植物种类。在本研究中，通过实时高分辨率质谱直接分析（DART-HRMS）对几种植物的顶空挥发物进行了分析。这些植物包括……（此处原文未完整列出植物名称）。DART-HRMS分析结果揭示了种内相似性和种间差异。使用主成分分析和全局分布随机邻域嵌入对数据进行探索性统计分析，结果显示相似物种聚类，不同物种分离。这导致使用监督随机森林（RF），从而得到了一个准确率为99%的模型。基于RF分类器创建了一个共形预测器，结果证明在显著性水平为8%、效率为0.1、观察到的模糊度为0且错误率为0的情况下是有效的。用于统计分析处理的变量根据使用主成分分析投影得分变量重要性和RF变量重要性指数实现聚类和区分物种的能力进行排序。然后将排名最高的变量确定为与先前在植物材料中鉴定出的分子一致的/值。因此，该技术为通过顶空分析创建用于检测和识别基于植物的新型毒品的数据库提供了概念验证。
