This is the Final Research Report of a project whose overall goal was to re-envision the traditional workflow for the analysis of controlled dangerous substances at the Maryland State Police-Forensic Science Division (MSP-FSD) and provide a safer, more rapid, more sensitive, and more selective approach.
This was done by evaluating the benefits and weaknesses of replacing current screening protocols with direct analysis real time mass spectrometry (DART-MS) and current confirmation protocols with gas chromatography- mass spectrometry (GC-MS) methods developed for specific drug classes. The project evaluated the efficacy of the two workflows on the analysis of powders that contained synthetic cannabinoids, synthetic cathinones, and opioids. Once the methods were established for the re-envisioned workflow, the current workflow and the re-envisioned workflow were compared. Four chemists within the MSP-FSD were trained to use DART-MS and the targeted GC-MS methods. The chemists were then given a set of 50 samples and asked to analyze half of each with the current workflow and half with the re-envisioned workflow. Analysts documented and timed all steps required for analyses they completed and interpreted the resulting data. A scoring system was developed and implemented as means to see differences in the methods. The results of this project benefit not only the MSP-FSD but the community in general. Creation of the DART-MS documentary package and the framework for GC-MS method development provide the community with tools to lower the barrier of adoption of these approaches. Adoption of targeted methods by other laboratories could provide increased data-sharing capabilities if retention time locking procedures are standardized. 11 figures, 8 tables, and 16 references
Downloads
Similar Publications
- IS2aR, a Computational Tool to Transform Voxelized Reference Phantoms into Patient-specific Whole-body Virtual CTs for Peripheral Dose Estimation
- Technical note: Measuring Bicondylar Length in Computed Tomography Data
- CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake Detection