Chemical Characterization of Emerging Designer Drugs
Jeremiah Morris, Johnson County Sheriffâ€™s Office Crime Lab
This project monitors and analyzes emerging designer drugs to provide forensic drug chemists with the analytical data necessary to identify synthetic cannabinoids and substituted cathinones.
Analytical data for emerging designer drugs of abuse (synthetic cannabinoids and substituted cathinones) will be collected during this project. Various websites which are known to sell designer drugs will be monitored throughout the study. As new substances are made available, samples of these substances will be purchased and their structures elucidated by NMR. Following structural elucidation, various types of analytical data will be obtained including GC/MS, LC/MS/MS, FTIR, and presumptive color test results. Analytical data for these compounds will be provided to forensic chemists to assist in the identification of unknown samples submitted with case submissions.
Development of a New Model to Study Firearms Related Blood Spatter
Michael Taylor, ESR, New Zealand, and Kevin Winer, Kansas City Police Crime Lab
This project focuses on the design and construction of a physical model to simulate the formation of gunshot-related blood spatter to answer case-related questions.
The study of gunshot-related blood spatter is a common and often critical task for investigators. Simulating the formation of this spatter to answer case-related questions is difficult. Furthermore the mechanism of spatter projection is not well understood. This project will address both these concerns. A novel physical model will be designed and constructed to study cranial gunshot wounding and spatter. Construction details will be made available to enable the replication of the model in the crime lab. In the course of testing the model, a comprehensive set of high-speed videos will be generated which will provide a valuable new teaching resource.
Random Probability Match Procedure for Statistical Comparison of Mass Spectral Data
Ruth Waddell-Smith, Michigan State University, in collaboration with the Alaska Scientific Crime Detection Laboratory in Anchorage, AK, and the Northeastern Illinois Regional Crime Laboratory in Vernon Hills, IL
This project targets the development of a method to determine the significance of associations in the comparison of evidence.
To address the lack of statistical evaluation of forensic evidence, an approach will be developed for the statistical comparison of mass spectral data obtained using gas chromatography-mass spectrometry (GC-MS). The approach will be based on classic probability theory, determining the random probability that a match between the mass spectrum of a questioned sample and that of a reference standard occurs by chance. While method development will use mass spectra of controlled substances, the procedure can be readily applied to mass spectral data for other types of forensic evidence.