Calculation of likelihood ratios in forensic glass comparisons; introduction to a R code and Shiny app applied to existing background glass elemental databases

The use of standardized and robust analytical methods for the quantitative analysis of the elemental composition of glass fragments enables the characterization and comparison of glass as forensic evidence. This paper introduces a new R-based Shiny graphical user interface (GUI) to calculate calibrated likelihood ratios (LRs) using three (3) different background databases of glass composition. We report, for the first time, a new vehicle survey glass database generated at Florida International University (FIU) generated from LA-ICP-MS analysis, a database comprised of a combination of casework and survey samples collected from solution-digestion ICP-MS analysis from the Federal Bureau of Investigation (FBI) Laboratory, and a previously reported casework sample database collected from LA-ICP-MS analysis at the Bundeskriminalamt (BKA) Laboratory. The LRs are calculated using a previously reported two-level multivariate kernel (MVK) model and calibrated using a previously described Pool Adjacent Violators (PAV) algorithm. The log LR (LLR) were calculated and compared to the match criterion recommended in the ASTM E2927-16e1 method, using these three background databases using a typical glass evidence case scenario. This paper also reports how the LLR values increase as the size of the background database increases, as expected. The R Shiny app and the new FIU vehicle background database are provided to researchers in the supplementary materials.

Automated summary

This paper introduces a new R-based Shiny GUI for calculating calibrated likelihood ratios (LRs) using three different background databases of glass composition, providing valuable insights into forensic glass analysis. The study demonstrates how LLR values increase with the size of the background database, highlighting the effectiveness of the method in forensic investigations. The R Shiny app and the newly introduced FIU vehicle background database are valuable resources for researchers in the field.