Learning algorithms to evaluate forensic glass evidence

Abstract

Glass fragments are often compared in the course of a forensic evaluation using their chemical composition determined with technologies such as LA-ICP-MS. At present forensic scientists advocate the use of two comparison criteria based on univariate intervals around all mean elemental concentrations for fragments originating from a known piece of broken glass. The main drawback of this approach is that it does not consider the dependencies between concentrations. Further, when the elemental concentrations are more variable within panes, it becomes harder to reject the null hypothesis of no difference between fragments. In the legal context higher variance would tend to incriminate the defendant because the intervals would tend to be wider. We demonstrate that a score-based approach to assess the probative value of evidence in glass comparisons outperforms the two standard interval methods and other methods proposed in the literature, at least in terms of minimizing classification error in the glass fragment sources we analyzed. We use machine learning algorithms to construct a similarity score between pairs of glass fragments. The learning algorithms exploit the dependencies among elemental concentrations and result in an empirical class probability; so, we can report the degree of similarity between two fragments. Our group is in the process of assembling the first glass composition database with enough information within and between glass samples to permit computing well-conditioned estimates of high-dimensional covariance matrices. These data will be available to anyone who wishes to carry out research in this area.