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Evaluation of the Incorporation of μ-XRF SDD Systems in Analytical Workflows of Black Electrical Tapes

NCJ Number
310368
Journal
Forensic Chemistry Volume: 42 Dated: 2025 Pages: 100638
Date Published
March 2025
Annotation

This study evaluates the incorporation of Micro-X-ray Fluorescence Spectrometry (µ-XRF) silicon drift detector (SDD) systems in analytical workflows of black electrical tapes.

Abstract

Micro-X-ray Fluorescence Spectrometry (µ-XRF), a technique widely adopted in forensic laboratories, has recently experienced a significant improvement with silicon drift detectors (SDDs); however, the research lag in this area has not caught up with the emergent instrumental modernization. This study expands the current body of knowledge by addressing this gap and evaluating the optimal workflow to incorporate µ-XRF SDD methods in electrical tape examinations. The findings of this study are anticipated to assist forensic laboratories with improved protocols. The experimental design evaluates sample handling and chemical data interpretation when the tape needs to undergo latent fingerprint development. The dataset includes contemporary electrical tape from 45 rolls produced in four countries, by seven manufacturers, by ten brands, and at various quality grades (high, medium, low); ten sections are sampled per roll. Pairwise comparisons (990) evaluate between-source discrimination and false inclusions. Also, the set contains five same-source rolls with 20 sections per roll to evaluate within-source variability and false exclusions (950 pairwise comparisons). Samples are examined by µ-XRF SDDs and three other conventional methods. Performance rates are reported for each technique alone and when used together to evaluate optimal combinations and analytical sequences. Due to high discrimination and classification abilities, it is recommended that µ-XRF analysis moves to the forefront of the analytical scheme after microscopic examination to optimize turnaround times, costs, and resources. Also, the study utilizes elemental ratio comparison intervals and quantitative similarity metrics that offer complementary information to reduce subjectivity in XRF spectral comparisons. (Published Abstract Provided)

Date Published: March 1, 2025