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Assessing the strengths and limitations of quantitative micromorphometry for the forensic examination of small arms propellant towards brand identification

NCJ Number
Forensic Science International Volume: 353 Dated: 2023
Date Published

This paper discusses the results from a study demonstrating that micromorphometry can provide valuable information to help determine the brand of small arms propellant.


Smalls arms propellants (SAP) also known as canister powders are readily accessible and cost-effective materials that firearms enthusiasts can acquire for the legitimate assembly of ammunition. These attributes also make SAPs advantageous for the construction of improvised explosive devices (IEDs). Thus, there is a need to develop robust metrics for the characterization of propellants to provide investigative leads as well as for comparisons between known and recovered residues. The goal of this research was to investigate the utility of a high-throughput, non-destructive, and low-cost quantitative automated image analysis routine for the characterization and discrimination of SAP. For this project, 204 one-pound canisters of smokeless propellant (powder) were acquired from local and online sources. These samples represent nine manufacturers and 154 unique brands. From this set, five brands were selected to assess the intra- and inter-lot variability. Eight parameters, which encompass size- and shape-dependent metrics, were measured for each sample. A total of approximately 85,000 granules were analyzed using linear discriminant analysis. A detailed assessment of the variables shows that the size-dependent metrics provide the greatest amount of sample discrimination. Overall accuracy of the method to correctly classify a test subset of data to the brand level is approximately 84.72 percent. The results from this study provide a framework in which to interpret smokeless propellant micromorphometry in the context of intelligence purposes for initial stages of criminal investigations, and for traditional comparisons between known and unknown samples. (Published Abstracts Provided)

Date Published: January 1, 2023