A compact Fourier-transform near-infrared spectrophotometer and chemometrics for characterizing a comprehensive set of seized ecstasy samples.
Jennifer A Cavalcante, Jamille C Souza, Jarbas J R Rohwedder, Adriano O. Maldaner, Celio Pasquini, Maria C Hespanhol
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy June 5, 2024 DOI: 10.1016/j.saa.2024.124163 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Method development and validation study Qualitative Peer reviewed |
|---|---|
| Population | Ecstasy tablets seized by the Brazilian Federal Police |
| Topics | MDMA |
| Keywords | Forensic analysis Illicit drugs Lda Pls-da Simca Forensic_analysis Drug_detection Portable_testing Spectroscopy |
| Citations | 3 |
| Key points | NIR spectroscopy with chemometric models can classify ecstasy tablets and quantify MDMA and MDA content with over 96% efficiency and low prediction errors. |
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.