This sixth episode of the “Strengthening the Forensic Workforce” season in the National Institute of Justice’s (NIJ’s) Just Science podcast series is an interview with Dr. Brooke Kammrath – a Professor of Forensic Science at the University of New Haven and Assistant Director of the Henry C. Lee Institute of Forensic Science – and Dri Tatiana Trejos – an Assistant Professor in the Department of Forensic and Investigative Science at West Virginia University – who discuss career paths for individuals trained in trace evidence analytical methods.
An Introductory Statement for the interview notes that trace evidence analysts are tasked with extracting information from small quantity samples such as gas, paint, fibers, and gunshot residue for the purpose of determining what might have occurred at a crime scene. These analysts use chemical, microscopic, and physical comparisons of evidence to reach conclusions and provide investigative leads. Dr. Kammrath and Dr. Trejos discuss available collegiate courses, such as microscopy and testimony practice, for those interested in trace evidence analysis and the education and training required to succeed in this work. They first discuss their own educational and professional backgrounds in forensic science, specifically forensic chemistry. This is followed by a review of trace evidence analysis and the types of evidence typically examined by trace analysts. Dr. Kammrath indicates that “trace” is used as an adjective applied to material evidence that has a small size or is in a small concentration, following actions or events that have occurred during a crime at a particular place. The accredited programs at each of the colleges represented is described. Some the changes in instructional methods that occurred during the pandemic are described.
- American Society of Crime Laboratory Directors Accreditation Initiative: Successes, Challenges, and Future Directions
- Nanomanipulation-Coupled Nanospray Mass Spectrometry Applied to the Extraction and Analysis of Trace Analytes Found on Fibers
- Two-Level Model for Evidence Evaluation in the Presence of Zeros