Abstract：
A comprehensive data set of ecstasy samples containing MDMA (N-methyl-3,4-methylenedioxyamphetamine) and MDA (3,4-methylenedioxyamphetamine) seized by the Brazilian Federal Police was characterized using spectral data obtained by a compact, low-cost, near-infrared Fourier-transform based spectrophotometer. Qualitative and quantitative characterization was accomplished using soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) classification, discriminating partial least square (PLS-DA), and regression models based on partial least square (PLS). By applying chemometric analysis, a protocol can be proposed for the in-field screening of seized ecstasy samples. The validation led to an efficiency superior to 96 % for ecstasy classification and estimating total actives, MDMA, and MDA content in the samples with a root mean square error of validation of 4.4, 4.2, and 2.7 % (m/m), respectively. The feasibility and drawbacks of the NIR technology applied to ecstasy characterization and the compromise between false positives and false negatives rate achieved by the classification models are discussed and a new approach to improve the classification robustness was proposed considering the forensic context.
摘要：
巴西联邦警察缉获的含有摇头丸（N-甲基-3,4-亚甲基二氧基苯丙胺）和丙二醛（3,4-亚甲基二氧基苯丙胺）的摇头丸样品的综合数据集使用紧凑的光谱数据进行了表征，低成本,近红外傅里叶变换分光光度计.定性和定量表征是使用类类比的软独立建模(SIMCA)完成的，线性判别分析(LDA)分类，判别偏最小二乘(PLS-DA)，和基于偏最小二乘(PLS)的回归模型。通过应用化学计量分析，可以提出一项方案，用于现场筛查缉获的摇头丸样品。验证导致摇头丸分类和估计总活性物质的效率优于96％，MDMA,样品中MDA含量的验证均方根误差为4.4、4.2和2.7％（m/m），分别。讨论了NIR技术应用于摇头丸表征的可行性和缺点，以及通过分类模型实现的假阳性和假阴性率之间的折衷，并考虑到取证背景，提出了一种提高分类鲁棒性的新方法。
