Dissertations, Theses, and Capstone Projects

Date of Degree

9-2022

Document Type

Dissertation

Degree Name

Ph.D.

Program

Criminal Justice

Advisor

Nicholas Petraco

Committee Members

Thomas Kubic

Jack Ballantyne

Nicholas Petraco

Subject Categories

Other Life Sciences | Other Statistics and Probability

Keywords

Dust, Trace Evidence, Microscopy, PLM, Stereomicroscope, Forensic Science, Statistics

Abstract

Evidence found at crime scenes is used to assist in creating a link the suspect, the victim, and the scene. As stated by the Locard’s Principle, every contact leaves a trace, that evidence can be used to link together an investigation. Traces are collected in hopes that they can be identified and associated to an individual or individuals to help solve that particular crime. However, the strongest conclusion for evidence traces is an association to a source, and unless a physical match of some kind is found, an individualization cannot be established even when known sample is available. However, having multiple associations across different types of evidence traces, used in conjunction, provides more probative information. This research examined multiple evidence traces, in the form of dust, as a collaboration to determine a possible link between a location and a sample. The traces were identified using stereomicroscopy and polarized light microscopy.

Collecting data was established by using a data sheet that tracked all possible components of the dust samples. Microscopically, samples were identified by physical and optical properties. Depending on the type of material, physical properties included identifying plant material, feathers, skin cells, etc. Additionally, material was grouped by color, shape, size, medullary patterns, and cross-sectional shapes. Different materials also have different optical properties such as birefringence, sign of elongation, and refractive indices. All of these characteristics together help to identify materials found in dust samples like fibers and minerals.

All together components were charted as being present or absent, and then each was statistically computed using Markov random fields to determine the probabilities of seeing these components together. A total of 500 samples from 100 rooms were viewed and analyzed. After each sample/aliquot was characterized, identified, and statistically evaluated by using microscopical and statistical analysis, it can be concluded there is a low probability of seeing specific components found together as compared to any other samples in the population. Additionally, 60% of the time, the local model correctly identified the questioned samples to known locations. Treating the local model as a database network, the correct location was found in the top 10 possibilities over 90% of the time allowing the microscopist to physically compare the questioned sample to 10 known locations to find a possible match.

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