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Detection of newly emerging psychoactive substances using Raman spectroscopy and chemometrics

机译：使用拉曼光谱法检测新出现的精神活性物质和化学计量学

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摘要

A novel approach for the identification of New Psychoactive Substances (NPS) by means of Raman spectroscopy coupled with Principal Components Analysis (PCA) employing the largest dataset of NPS reference materials to date is reported here. Fifty three NPS were selected as a structurally diverse subset from an original dataset of 478 NPS compounds. The Raman spectral profiles were experimentally acquired for all 53 substances, evaluated using a number of pre-processing techniques, and used to generate a PCA model. The optimum model system used a relatively narrow spectral range (1300-1750 cm(-1)) and accounted for 37% of the variance in the dataset using the first three principal components, despite the large structural diversity inherent in the NPS subset. Nonetheless, structurally similar NPS (i.e., the synthetic cannabinoids FDU-PB-22 & NM-2201) grouped together in the PCA model based on their Raman spectral profiles, while NPS with different chemical scaffolds (i.e., the benzodiazepine flubromazolam and the cathinone -PBT) were well delineated, occupying markedly different areas of the three-dimensional scores plot. Classification of NPS based on their Raman spectra (i.e., chemical scaffolds) using the PCA model was further investigated. NPS that were present in the initial dataset of 478 NPS but were not part of the selected 53 training set (validation set) were observed to be closely aligned to structurally similar NPS within the generated model system in all cases. Furthermore, NPS that were not present in the original dataset of 478 NPS (test set) were also shown to group as expected in the model (i.e., methamphetamine and N-ethylamphetamine). This indicates that, for the first time, a model system can be applied to potential unknown' psychoactive substances, which are new to the market and absent from existing chemical libraries, to identify key structural features to make a preliminary classification. Consequently, it is anticipated that this study will be of interest to the broad scientific audience working with large structurally diverse chemical datasets and particularly to law enforcement agencies and associated scientific analytical bodies worldwide investigating the development of novel identification methodologies for psychoactive substances.
机译：通过拉曼的装置，用于新的精神药物（NPS）的识别的新方法光谱加上采用的NPS参考资料迄今为止最大的数据集在这里报道，主成分分析（PCA）。五十3 NPS被选定为从478种NPS化合物的原始数据集结构多样的子集。拉曼光谱图进行了实验获得的所有53层的物质，使用多种预处理技术评估，并用于产生一个PCA模型。最佳模型系统中使用的相对较窄的光谱范围（一三○○年至1750年厘米（-1））和占使用前三个主成分的数据集的方差的37％，尽管在NPS子集所固有的大的结构多样性。然而，结构相似的NPS（即，合成大麻素FDU-PB-22＆NM-2201）在基于其拉曼光谱曲线的PCA模型组合在一起，而NPS具有不同化学支架（即，苯二氮卓flubromazolam和卡西酮 - PBT）的良好划定，占据三维得分图的显着不同的区域。基于使用PCA模型的拉曼光谱（即，化学支架）NPS的分类被进一步研究。观察到的是NPS中存在的NPS 478的初始数据集，但不是所选择的53训练集（验证集）的一部分被紧密结合到在所有情况下所生成的模型系统内的结构上相似的NPS。此外，即不存在于478个NPS（试验组）的原始数据集NPS也显示出基作为预期在模型（即，甲基苯丙胺和N- ethylamphetamine）。这表明，对于第一次，一个模型系统可以应用到潜在的未知”的精神活性物质，这是新的市场，并没有从现有的化学库，找出关键结构特征进行了初步分类。因此，可以预料，这项研究将有兴趣了广大观众的科学工作与大型结构不同的化学数据集，特别是执法机构和相关的科学分析机构全球调查新颖的识别方法为精神活性物质的开发。

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  • 来源
    《RSC Advances》 |2018年第56期|共10页
  • 作者

    Calvo-Castro Jesus; Guirguis Amira; Samaras Eleftherios G.; Zloh Mire; Kirton Stewart B.; Stair Jacqueline L.;

  • 作者单位

    Univ Hertfordshire Sch Life &

    Med Sci Dept Pharm Pharmacol &

    Postgrad Med Hatfield AL10 9AB Herts England;

    Univ Hertfordshire Sch Life &

    Med Sci Dept Pharm Pharmacol &

    Postgrad Med Hatfield AL10 9AB Herts England;

    Univ Hertfordshire Sch Life &

    Med Sci Dept Pharm Pharmacol &

    Postgrad Med Hatfield AL10 9AB Herts England;

    Univ Hertfordshire Sch Life &

    Med Sci Dept Pharm Pharmacol &

    Postgrad Med Hatfield AL10 9AB Herts England;

    Univ Hertfordshire Sch Life &

    Med Sci Dept Pharm Pharmacol &

    Postgrad Med Hatfield AL10 9AB Herts England;

    Univ Hertfordshire Sch Life &

    Med Sci Dept Pharm Pharmacol &

    Postgrad Med Hatfield AL10 9AB Herts England;

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  • 正文语种 eng
  • 中图分类 化学;
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