JOURNAL ARTICLE
False‐negative probability in the SEM/EDS automated discovery of iGSR particles: A Bayesian approach.
Published In: Journal of Forensic Sciences, 2023, v. 68, n. 5. P. 1792 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Onetto, Martín A.; Carignano, Edgardo; Pregliasco, Rodolfo G. 3 of 3
Abstract
The automated search software integrated with a scanning electron microscope (SEM/EDS) has been the standard tool for detecting inorganic gunshot residues (iGSR) for several decades. The detection of these particles depends on various factors such as collection, preservation, contamination with organic matter, and the method for sample analysis. This article focuses on the influence of equipment resolution setup on the backscattered electron images of the sample. The pixel size of these images plays a crucial role in determining the detectability of iGSR particles, especially those with sizes close to the pixel size. In this study, we calculated the probability of missing all characteristic iGSR particles in a sample using an SEM/EDS automated search and how it depends on the image pixel resolution setup. We developed and validated an iGSR particle detection model that links particle size with equipment registers and applied it to 320 samples analyzed by a forensic science laboratory. Our results show that the probability of missing all characteristic iGSR particles due to their size is below 5% for pixel sizes below 0.32 μm2. These findings indicate that pixel sizes as large as twice the one commonly used in laboratory casework, that is, 0.16 μm2, are effective for initial sample scanning, yielding good detection rates of characteristic particles that could exponentially reduce laboratory workload. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Forensic Sciences. 2023/09, Vol. 68, Issue 5, p1792
- Document Type:Article
- Subject Area:Science
- Publication Date:2023
- ISSN:0022-1198
- DOI:10.1111/1556-4029.15323
- Accession Number:171386803
- Copyright Statement:Copyright of Journal of Forensic Sciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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