The Use of LA-ICP-MS Databases to Calculate Likelihood Ratios for the Forensic Analysis of Glass Evidence

• NCJ Number: 252067
• Author(s): Ruthmara Corzo, Tricia Hoffman, Peter Weis, Javier Franco-Pedroso, Daniel Ramos, Jose Almirall
• Date Published: May 2018
• Length: 7 pages
• Agencies: NIJ-Sponsored
• Award: Strengthening the evaluation and interpretation of glass evidence using statistical analysis of collection sets and databases of refractive index and elemental data (uXRF, ICP-MS and LA-ICP-MS)

Annotation

This study used two glass databases to evaluate the performance of the likelihood ratio, which provides a quantitative measure of the value of the evidence in support of any hypothesis and accounts for the rarity of an elemental profile through the use of a glass database.

Abstract

Laser ablation-inductively coupled plasma-mass spectrometry (LA-CP-MS) has been shown to be an excellent technique for the discrimination of glass originating from different sources and for the association of glass originating from the same source. Typically, a match criterion is used to compare the elemental profile of the known sample to a questioned sample, and if the glass samples are determined to match, this may be followed by the use of a verbal scale to report the forensic practitioner’s conclusion. This approach has several disadvantages; a fixed match criterion suffers from the “fall-off-the-cliff effect,” the rarity of an elemental profile is not taken into account, and the use of a verbal scale to assign a weight of evidence may be considered as subjective and can vary by examiner. An alternative approach includes the use of a continuous likelihood ratio that provides a quantitative measure of the value of the evidence in support of any hypothesis and accounts for the rarity of an elemental profile through the use of a glass database. In the current study, the first database used includes 420 automotive windshield samples, and the second database used includes 385 glass samples from casework. The multivariate kernel model was used for the calculation of the likelihood ratio; however, this model led to unreasonably large (or small) likelihood ratios. Thus, a calibration step, using the Pool Adjacent Violators (PAV) algorithm, was necessary in order to limit the likelihood ratio to reasonable values. The calibrated likelihood ratio presented rates of misleading evidence of <1.5 percent (for LRs<1 when objects came from the same source), and of <1.0 percent (for LRs>1 when objects came from different sources), which improved over the analogous ASTM false inclusion and false exclusion rates previously reported. In addition, the likelihood ratio limited the magnitude of the misleading evidence, providing only weak to moderate support for the incorrect hypothesis. Finally, most of the pairs found to present LR>1 when objects originated from different sources were explained by similarity of manufacturer of the glass source. 1 figure (Publisher abstract modified)