Presentation Type
Invited
Student
No
Track
Forensic Statistics
Abstract
Aluminum (Al) powder is often used as a fuel in explosive devices; therefore, individuals attempting to make illegal improvised explosive devices often obtain it from legitimate commercial products or make it themselves using readily available Al starting materials. The characterization and differentiation between sources of Al powder for additional investigative and intelligence value has become increasingly important. Previous research modeled the distributions of micromorphometric features of Al powder particles within a subsample to support Al source discrimination. Since then, additional powder samples from a variety of different source types have been obtained and analyzed, providing a more comprehensive dataset for applying the two statistical methods for interpretation and discrimination of source. Here, we compare two different statistical techniques: one using linear discriminant analysis (LDA), and the other using a modification to the method used in ASTM E2927-16e1 and E2330-19. The LDA method results in an Al source classification for each questioned sample. Alternatively, our modification to the ASTM method uses an interval-based match criterion to associate or exclude each of the known sources as the actual source of a trace. Although the outcomes of these two statistical methods are fundamentally different, their performance with respect to the closed-set identification of source problem is compared. Additionally, the modified ASTM method will be adapted to provide a vector of scores in lieu of the binary decision as the first step towards a score-based likelihood ratio for interpreting Al powder micromorphometric measurement data.
Start Date
2-7-2023 11:00 AM
End Date
2-7-2023 12:00 PM
Session 8: Statistical Discrimination Methods for Forensic Source Interpretation of Aluminum Powders in Explosives
Pasque 255
Aluminum (Al) powder is often used as a fuel in explosive devices; therefore, individuals attempting to make illegal improvised explosive devices often obtain it from legitimate commercial products or make it themselves using readily available Al starting materials. The characterization and differentiation between sources of Al powder for additional investigative and intelligence value has become increasingly important. Previous research modeled the distributions of micromorphometric features of Al powder particles within a subsample to support Al source discrimination. Since then, additional powder samples from a variety of different source types have been obtained and analyzed, providing a more comprehensive dataset for applying the two statistical methods for interpretation and discrimination of source. Here, we compare two different statistical techniques: one using linear discriminant analysis (LDA), and the other using a modification to the method used in ASTM E2927-16e1 and E2330-19. The LDA method results in an Al source classification for each questioned sample. Alternatively, our modification to the ASTM method uses an interval-based match criterion to associate or exclude each of the known sources as the actual source of a trace. Although the outcomes of these two statistical methods are fundamentally different, their performance with respect to the closed-set identification of source problem is compared. Additionally, the modified ASTM method will be adapted to provide a vector of scores in lieu of the binary decision as the first step towards a score-based likelihood ratio for interpreting Al powder micromorphometric measurement data.