From binary presumptive assays to probabilistic assessments: Differentiation of shooters from non-shooters using IMS, OGSR, neural networks, and likelihood ratios

by Kenneth Ujevich

Screening tests are used in forensic science for field testing and directing laboratory analysis of physical evidence. These tests are often binary in that the data produced is interpreted as yes/no or present/absent. This research describes the results of using ion mobility spectrometry (IMS) and hand swab samples collected from 73 individuals to differentiate shooters from non-shooters by targeting organic constituents of firearms discharge residues. Each individual completed a questionnaire regarding their personal hobbies to accurately explain positive test results. Pattern matching was undertaken using neural networks, which are well-suited to classification and prediction tasks across large data sets. Decision thresholds were established using likelihood ratios derived from the population study. This approach significantly reduced the background positive rates compared to an arbitrary decision threshold technique. This methodology could be extended to other pattern-recognition algorithms used with instrumental data. This experiment also reports the largest population study to date focused on the organic residues of firearms discharge. The proportion of positives found in the population sample were less than 5%; when a likelihood ratio of 10:1 (shooter/not shooter) was used, the frequency of positives fell below 2%. The results suggest that background levels of organic gunshot residue will not be a significant analytic concern for assay development.

Suzanne Bell, Lauren Seitzinger. “From binary presumptive assays to probabilistic assessments: Differentiation of shooters from non-shooters using IMS, OGSR, neural networks, and likelihood ratios.” Forensic Science International, Volume 263, June 2016, Pages 176-185, ISSN 0379-0738, http://dx.doi.org/10.1016/j.forsciint.2016.04.020.

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