Marjan Sjerps

Interpretation of Forensic Evidence

One of the central questions in a legal trial is whether the suspect did or did not commit the crime. It will be apparent that absolute certainty cannot be attained. Because there is always a certain degree of uncertainty when interpreting the evidence, none of the evidence rules out all hypotheses except one. The central question should therefore be formulated in terms of probability. For instance, how probable is it that the suspect is the offender, given the situation and a number of inherent uncertain pieces of evidence? The answer to this question requires the estimation, and subsequent combination, of all relevant probabilities, and cannot be provided by the forensic expert. What the forensic expert can provide is just a piece of the puzzle: an estimate of the evidential value of her investigation. This evidential value is based on estimates of the probabilities of the evidence given at least two prespecified hypotheses. These probabilities can subsequently be used by the legal decision maker in order to determine an answer to the question above, but they are, of course, not sufficient. They need to be combined with all the other information in the case. A probabilistic framework to do this is the Likelihood Ratio approach for the interpretation of forensic evidence. In this chapter we will describe this framework.

Error rates in forensic DNA analysis: Definition, numbers, impact and communication

Forensic DNA casework is currently regarded as one of the most important types of forensic evidence, and important decisions in intelligence and justice are based on it. However, errors occasionally occur and may have very serious consequences. In other domains, error rates have been defined and published. The forensic domain is lagging behind concerning this transparency for various reasons. In this paper we provide definitions and observed frequencies for different types of errors at the Human Biological Traces Department of the Netherlands Forensic Institute (NFI) over the years 2008-2012. Furthermore, we assess their actual and potential impact and describe how the NFI deals with the communication of these numbers to the legal justice system. We conclude that the observed relative frequency of quality failures is comparable to studies from clinical laboratories and genetic testing centres. Furthermore, this frequency is constant over the five-year study period. The most common causes of failures related to the laboratory process were contamination and human error. Most human errors could be corrected, whereas gross contamination in crime samples often resulted in irreversible consequences. Hence this type of contamination is identified as the most significant source of error. Of the known contamination incidents, most were detected by the NFI quality control system before the report was issued to the authorities, and thus did not lead to flawed decisions like false convictions. However in a very limited number of cases crucial errors were detected after the report was issued, sometimes with severe consequences. Many of these errors were made in the post-analytical phase. The error rates reported in this paper are useful for quality improvement and benchmarking, and contribute to an open research culture that promotes public trust. However, they are irrelevant in the context of a particular case. Here case-specific probabilities of undetected errors are needed. These should be reported, separately from the match probability, when requested by the court or when there are internal or external indications for error. It should also be made clear that there are various other issues to consider, like DNA transfer. Forensic statistical models, in particular Bayesian networks, may be useful to take the various uncertainties into account and demonstrate their effects on the evidential value of the forensic DNA results.

On the consequences of DNA profile mismatches for close relatives of an excluded suspect

Abstract If the DNA profiles of a crime stain and the reference sample from the suspect do not match, the suspect is excluded as the donor of the crime stain. However, in some situations the DNA evidence can suggest that a close relative of the suspect might match the stain, in particular when the reference sample from the suspect and the crime stain share rare alleles. This finding can be important for the authorities. The forensic scientist has to decide whether or not to notify the authorities in these circumstances. To the best of our knowledge there is not yet an objective rule for making this decision. We propose such a decision rule for brothers of the suspect, investigate its performance and address some ethical, legal, and practical aspects. Our calculations can be simply adjusted for other relatives of the suspect.

A Dutch population study of the STR Loci HUMTHO1, HUMFES/FPS, HUMVWA31/1 and HUMF13A1, conducted for forensic purposes.

We report on a Dutch population study of the STR loci HUMTHOI, HUMFES/FPS, HUMVWA31/1, and HUMF13A1, in which we used multiplex amplification and automated fragment detection. Genotype and allele frequencies showed no deviation from Hardy-Weinberg and linkage equilibrium. The improved Bonferroni procedure was used to combine the results of several tests. The power of discrimination of a complete profile exceeded 0.9998. We compared the allele frequencies in the Dutch sample to the frequencies in other populations using a biplot to visualize alleles and populations simultaneously. The Dutch sample was similar to most other Caucasian samples. The data demonstrate that the genetic systems in this report are a valuable tool for forensic identity testing in The Netherlands.

Class-conditional feature modeling for ignitable liquid classification with substantial substrate contribution in fire debris analysis

Forensic chemical analysis of fire debris addresses the question of whether ignitable liquid residue is present in a sample and, if so, what type. Evidence evaluation regarding this question is complicated by interference from pyrolysis products of the substrate materials present in a fire. A method is developed to derive a set of class-conditional features for the evaluation of such complex samples. The use of a forensic reference collection allows characterization of the variation in complex mixtures of substrate materials and ignitable liquids even when the dominant feature is not specific to an ignitable liquid. Making use of a novel method for data imputation under complex mixing conditions, a distribution is modeled for the variation between pairs of samples containing similar ignitable liquid residues. Examining the covariance of variables within the different classes allows different weights to be placed on features more important in discerning the presence of a particular ignitable liquid residue. Performance of the method is evaluated using a database of total ion spectrum (TIS) measurements of ignitable liquid and fire debris samples. These measurements include 119 nominal masses measured by GC-MS and averaged across a chromatographic profile. Ignitable liquids are labeled using the American Society for Testing and Materials (ASTM) E1618 standard class definitions. Statistical analysis is performed in the class-conditional feature space wherein new forensic traces are represented based on their likeness to known samples contained in a forensic reference collection. The demonstrated method uses forensic reference data as the basis of probabilistic statements concerning the likelihood of the obtained analytical results given the presence of ignitable liquid residue of each of the ASTM classes (including a substrate only class). When prior probabilities of these classes can be assumed, these likelihoods can be connected to class probabilities. In order to compare the performance of this method to previous work, a uniform prior was assumed, resulting in an 81% accuracy for an independent test of 129 real burn samples.

Probabilistic peak detection for first-order chromatographic data

We present a novel algorithm for probabilistic peak detection in first-order chromatographic data. Unlike conventional methods that deliver a binary answer pertaining to the expected presence or absence of a chromatographic peak, our method calculates the probability of a point being affected by such a peak. The algorithm makes use of chromatographic information (i.e. the expected width of a single peak and the standard deviation of baseline noise). As prior information of the existence of a peak in a chromatographic run, we make use of the statistical overlap theory. We formulate an exhaustive set of mutually exclusive hypotheses concerning presence or absence of different peak configurations. These models are evaluated by fitting a segment of chromatographic data by least-squares. The evaluation of these competing hypotheses can be performed as a Bayesian inferential task. We outline the potential advantages of adopting this approach for peak detection and provide several examples of both improved performance and increased flexibility afforded by our approach.

The use of δ2H and δ18O isotopic analyses combined with chemometrics as a traceability tool for the geographical origin of bell peppers

Two approaches were investigated to discriminate between bell peppers of different geographic origins. Firstly, δ(18)O fruit water and corresponding source water were analyzed and correlated to the regional GNIP (Global Network of Isotopes in Precipitation) values. The water and GNIP data showed good correlation with the pepper data, with constant isotope fractionation of about -4. Secondly, compound-specific stable hydrogen isotope data was used for classification. Using n-alkane fingerprinting data, both linear discriminant analysis (LDA) and a likelihood-based classification, using the kernel-density smoothed data, were developed to discriminate between peppers from different origins. Both methods were evaluated using the δ(2)H values and n-alkanes relative composition as variables. Misclassification rates were calculated using a Monte-Carlo 5-fold cross-validation procedure. Comparable overall classification performance was achieved, however, the two methods showed sensitivity to different samples. The combined values of δ(2)H IRMS, and complimentary information regarding the relative abundance of four main alkanes in bell pepper fruit water, has proven effective for geographic origin discrimination. Evaluation of the rarity of observing particular ranges for these characteristics could be used to make quantitative assertions regarding geographic origin of bell peppers and, therefore, have a role in verifying compliance with labeling of geographical origin.

A dutch population study of the STR loci D21S11 and HUMFIBRA

To introduce a duplex PCR system consisting of the STR loci D21S11 and HUMFIBRA in forensic identity testing we analysed a Dutch Caucasian database of 205 individuals. The combined power of discrimination of the two loci is 0.9978 and there was no evidence for linkage equilibrium between the two loci (p=0.91). However, we noticed departure from Hardy-Weinberg equilibrium for the D21S11-locus in our database (p=0.03) but the differences between observed and expected D21S11 allele pair frequencies were of negligible practical significance in forensic calculations.

Dutch Caucasian population data on the loci LDLR, GYPA, HBGG, D7S8, and GC

To introduce the loci LDLR, GYPA, HBGG, D7S8, and GC (PM loci) in Dutch forensic identity testing, allele and genotype frequencies were determined in a Dutch Caucasian population sample, which had previously been typed for the HLADQA1 locus [12]. All 6 loci met Hardy-Weinberg equilibrium expectations, and there is little evidence for association between pairs of loci. The combined power of discrimination for all 6 loci is 0.9997. The allele frequencies of the PM loci were similar to 2 other Caucasian populations [3, 10], and differed from 3 non-Caucasian populations [3].

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.