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首页> 外文期刊>ACS Omega >Random Forest Processing of Direct Analysis in Real-Time Mass Spectrometric Data Enables Species Identification of Psychoactive Plants from Their Headspace Chemical Signatures
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Random Forest Processing of Direct Analysis in Real-Time Mass Spectrometric Data Enables Species Identification of Psychoactive Plants from Their Headspace Chemical Signatures

机译：实时质谱数据直接分析中的随机森林处理，可从其顶空化学特征识别精神活性植物。

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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.
机译：联合国毒品和犯罪问题办公室指定了几项“法律上的崇高”作为“关注植物”，因为它们有越来越多的滥用娱乐行为的危险。由于很难将它们与无害的植物材料（如食品，药草和香料）区分开来，因此无法常规识别这些产品。在此证明，这些产品中的几种具有独特但一致的顶空化学特征，并且可以使用对其化学特征进行的多元统计分析处理来准确地识别衍生出该物质的植物的种类。对于本研究，通过实时高分辨率高分辨率质谱法（DART-HRMS）中的直接分析来分析几种物种的顶空挥发物。这些物种包括Althaea of​​ficinalis，Calea zacatechichi，Cannabis indica，sativa，Echinopsis pachanoi，Lactuca virosa，Leonotis leonurus，Mimosa hositlis，Mitragyna speciosa，Ocimum basilicum，Origanum vulgare，Pipe methysticum，Turnera acussticum，Turnera usa，Salvia。 DART-HRMS分析的结果显示出种内相似性和种间差异。使用主成分分析和全局t分布随机邻居嵌入对数据进行的探索性统计分析表明，相似物种聚集并且不同物种分离。这导致使用监督随机森林（RF），从而产生了具有99％准确性的模型。创建了基于RF分类器的共形预测器，并证明其对8％的显着性水平有效，效率为0.1，观察到的模糊度为0，错误率为0。用于统计分析处理的变量为使用主成分分析在物种之间进行聚类和区分的能力方面排名–投影得分的可变重要性和RF可变重要性指数。然后将排名最高的变量确定为m / z值，该值与先前在植物材料中确定的分子一致。因此，该技术显示了用于创建数据库的概念验证，该数据库用于通过顶空分析来检测和识别基于植物的合法高价。

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