期刊
FORENSIC SCIENCE INTERNATIONAL
卷 302, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.forsciint.2019.109900
关键词
NPS isomers; VUV-spectroscopy; Forensic drug analysis; ROC-curves; Likelihood Ratio; Evidential value
Currently, forensic drug experts are facing chemical identification challenges with the increasing number of new isomeric forms of psychoactive substances occurring in case samples. Very similar mass spectra for these substances could easily result in misidentification using the regular GC-MS screening methods in combination with colorimetric testing in forensic laboratories. Building on recent work from other groups, this study demonstrates that GC-VUV is a powerful technique for drug isomer differentiation, showing reproducible and discriminating spectra for aromatic ring-isomers. MS and VUV show complementary selectivity as VUV spectra are ring-position specific whereas MS spectra are characteristic for the amine moieties of the molecule. VUV spectra are very reproducible showing less than 0.1 parts per thousand deviation in library match scores and therefore small spectral differences suffice to confidently distinguish isomers. In comparison, MS match scores gave over 10 parts per thousand deviation and showed significant overlap in match score ranges for several isomers. This poses a risk for false positive identifications when assigning compounds based on retention time and GC-MS mass spectrum. A strategy was developed, based on Kernel Density Estimations of match scores, to construct Receiver Operating Characteristic (ROC) curves and estimate likelihood ratios (LR values) with respect to the chemical differentiation of drug related isomers. This approach, and the added value of GC-VUV is demonstrated with the chemical analysis of several samples from drug case work from the Amsterdam area involving both compounds listed in Dutch drug legislation (3,4-MDMA; 3,4-MDA; 4-MMC; 4-MEC and 4-FA) as well as their unlisted and thus uncontrolled isomers (2,3-MDMA; 2,3-MDA; 2- and 3-MMC; 2- and 3-MEC and 2- and 3-FA). (C) 2019 Elsevier B.V. All rights reserved.
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