Abstract

The ability to associate a smokeless powder, smokeless powder residue, or organic gunshot residue (OGSR) to one another may be helpful in determining the origin of a suspected sample and aid in linking a suspect to a crime scene. In this study, smokeless powders were extracted and analyzed via gas chromatography – mass spectrometry (GC-MS) and direct analysis in real time – high resolution mass spectrometry (DART-HRMS). Subsequently, group definition was performed using hierarchical cluster analysis and principal component analysis followed by internally validated classification models. Then, smokeless powder residues were generated in-lab and extracted. Resulting residue data from each instrument was classified within the respective smokeless powder model using linear discriminant analysis (LDA) with external test sets. Residue groupings and classification models were also generated. Ammunition was loaded with known smokeless powder, then fired to collect OGSR from cloth targets. The OGSR was extracted and analyzed via DART-HRMS and GC-MS, then tested against the smokeless powder and residue models to determine the association of OGSR to its intact smokeless powder as well as to lab generated residues. Reference classes for the OGSR samples in the LDA prediction were determined via flow charts for informed analyst determination of class in smokeless powder and residue models. Standards of common smokeless powder components were pyrolyzed and an expected pyrolysis products profile was created for each sample based on the intact composition. Similarity and correlation metrics including Pearson's correlation, Sørensen-Dice similarity coefficient, and Concordance correlation were evaluated in the comparison of smokeless powder to residue and residue to expected pyrolysis products. Pearson's correlation was used in the comparison of smokeless powder to OGSR and smokeless powder residue to OGSR.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2022

Semester

Spring

Advisor

Bridge, Candice

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Department

Chemistry

Degree Program

Chemistry

Format

application/pdf

Identifier

CFE0009447; DP0027170

URL

https://purls.library.ucf.edu/go/DP0027170

Language

English

Release Date

November 2022

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Download

Share

COinS