Peter Vergeer

Likelihood ratio methods for forensic comparison of evaporated gasoline residues

In the investigation of arson, evidence connecting a suspect to the fire scene may be obtained by comparing the composition of ignitable liquid residues found at the crime scene to ignitable liquids found in possession of the suspect. Interpreting the result of such a comparison is hampered by processes at the crime scene that result in evaporation, matrix interference, and microbial degradation of the ignitable liquid. Most commonly, gasoline is used as a fire accelerant in arson. In the current scientific literature on gasoline comparison, classification studies are reported for unevaporated and evaporated gasoline residues. In these studies the goal is to discriminate between samples of several sources of gasoline, based on a chemical analysis. While in classification studies the focus is on discrimination of gasolines, for forensic purposes a likelihood ratio approach is more relevant. In this work, a first step is made towards the ultimate goal of obtaining numerical values for the strength of evidence for the inference of identity of source in gasoline comparisons. Three likelihood ratio methods are presented for the comparison of evaporated gasoline residues (up to 75% weight loss under laboratory conditions). Two methods based on distance functions and one multivariate method were developed. The performance of the three methods is characterized by rates of misleading evidence, an analysis of the calibration and an information theoretical analysis. The three methods show strong improvement of discrimination as compared with a completely uninformative method. The two distance functions perform better than the multivariate method, in terms of discrimination and rates of misleading evidence.

Performance Study of a Score-based Likelihood Ratio System for Forensic Fingermark Comparison

In this article, the performance of a score‐based likelihood ratio (LR) system for comparisons of fingerprints with fingermarks is studied. The system is based on an automated fingerprint identification system (AFIS) comparison algorithm and focuses on fingerprint comparisons where the fingermarks contain 6–11 minutiae. The hypotheses under consideration are evaluated at the level of the person, not the finger. The LRs are presented with bootstrap intervals indicating the sampling uncertainty involved. Several aspects of the performance are measured: leave‐one‐out cross‐validation is applied, and rates of misleading evidence are studied in two ways. A simulation study is performed to study the coverage of the bootstrap intervals. The results indicate that the evidential strength for same source comparisons that do not meet the Dutch twelve‐point standard may be substantial. The methods used can be generalized to measure the performance of score‐based LR systems in other fields of forensic science.

Numerical likelihood ratios outputted by LR systems are often based on extrapolation: When to stop extrapolating?

A recent trend in forensic science is the development of objective, automated systems for the comparison of trace and reference material that give as output numerical likelihood ratios (LRs). For well discriminating LR systems, often the probability of the evidence given one or the other hypothesis depends on the density from the tail of a probability distribution. The models for probability distributions are trained by data. Since there is no proof of the applicability of the models beyond the data range, LR systems are sensitive to extrapolation errors. Given the unknown behavior in the tail region one may define the problem as when to stop extrapolating. When applied to LR systems, this leads to limit values of the likelihood ratio (e.g. a minimum and a maximum value of the LR outputted by the LR system), depending on the sizes of the validation datasets used. The solution proposed in this paper to determine these limits is based on the normalized Bayes error-rate [1] in combination with the introduction of misleading LRs with increasing strength.

Towards source level evaluation of the evidential value of fibre examinations

This paper aims to provide the first steps towards a numerical source level evaluation of fibre evidence. For that purpose, likelihood ratio equations are derived for four generic scenarios, in which the source frequency, the number of references and trace types investigated, and the number of matches vary. Previous experimental studies into the evaluation of fibre evidence are reviewed and we demonstrate how the results of these studies, as well as other data, can be used to evaluate the derived equations for the four scenarios. Evaluation is not straightforward and requires a number of assumptions. This is mainly because the relevant population under consideration in a specific case cannot be sufficiently evaluated. In addition, the subjective match-criterion in current forensic fibre examinations makes it impossible to implement a good evaluation of the within-variation of samples. As a result, the discrimination power, currently calculated for discrimination studies, is only valid for samples with negligible heterogeneity. We conclude that reporting a numerical evidential value for forensic fibre examinations is not yet feasible as the data are available for only a few types of fibres and cannot be used without several assumptions. We propose a number of developments that are required to improve the accuracy and numerical analysis.

Quantifying Uncertainty in Estimations of the Total Weight of Drugs in Groups of Complex Matrices: Using the Welch–Satterthwaite Equation

In this paper, a method is described to quantify estimations of the total amount of drugs in groups of seized items, including quantification of the precision. Previous work on this topic was based on the assumptions of normally distributed measurements and grouping of items with a common relative standard deviation. In practice, these assumptions are often violated, for example, for data with point masses at 0, or if certain items in a group have a very high standard deviation. The method described in this paper is based on work by Welch and Satterthwaite and does not assume constant relative standard deviations. Case examples are described for which the method is applied, and simulation studies are carried out for which both methods are applied. In the cases, both methods perform reasonably well. If the assumption of common relative standard deviations clearly does not apply, it is advised to use the method described.