Ecstasy is a recreational drug known to have serious adverse effects, necessitating the development of new techniques for its rapid and affordable identification in harm reduction policies and forensic laboratories. In this study, ATR-FTIR spectroscopy and DD-SIMCA (Data Driven Soft Independent Modeling of Class Analogy) were employed to determine the profile of ecstasy tablets and their adulterants. ATR-FTIR spectroscopy, which is a fast and non-destructive method that provides structural information, and DD-SIMCA, a supervised technique that distinguishes between different groups, were used. The Hierarchical Cluster Analysis (HCA) revealed the formation of two primary clusters: one comprising mainly MDA samples with some MDMA samples, and the second including cocaine, methamphetamine, and caffeine samples, as well as a mixture of MDA samples with methamphetamine and caffeine. Principal Component Analysis (PCA) was performed, and the first five principal components (PC) explained 86.25% of the total data variance. In the PC1xPC2 scores plot, the distribution of samples containing MDs (MDA and MDMA) was observed. Further analysis using PCA revealed the grouping of cocaine samples into two separate groups based on variations in concentration. The DD-SIMCA model demonstrated high sensitivity values in accurately identifying target samples (MDA) and relatively high specificity values in both the training and test sets. The study highlights the effectiveness of ATR-FTIR spectroscopy, PCA, and DD-SIMCA for the precise classification of ecstasy and its adulterants and suggests their potential in drug identification and analysis.

摇头丸是一种已知具有严重副作用的娱乐性药物，因此需要开发新技术，以便在减少伤害政策和法医实验室中快速且经济地识别摇头丸。在本研究中，采用 ATR-FTIR 光谱和 DD-SIMCA（数据驱动的软独立类比建模）来确定摇头丸片及其掺假品的特征。使用了 ATR-FTIR 光谱（一种提供结构信息的快速且无损的方法）和 DD-SIMCA（一种区分不同组的监督技术）。层次聚类分析 (HCA) 揭示了两个主要聚类的形成：一个主要包含 MDA 样品和一些 MDMA 样品，第二个包含可卡因、甲基苯丙胺和咖啡因样品，以及 MDA 样品与甲基苯丙胺和咖啡因的混合物。进行主成分分析（PCA），前五个主成分（PC）解释了总数据方差的86.25%。在 PC1xPC2 分数图中，观察到含有 MD（MDA 和 MDMA）的样品的分布。使用主成分分析 (PCA) 的进一步分析显示，根据浓度变化，可卡因样品分为两个不同的组。DD-SIMCA 模型在准确识别目标样本（MDA）方面表现出较高的灵敏度，并且在训练和测试集中表现出相对较高的特异性。该研究强调了 ATR-FTIR 光谱、PCA 和 DD-SIMCA 在摇头丸及其掺假品精确分类方面的有效性，并表明它们在药物鉴定和分析中的潜力。