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Berger C.E.H.,Netherlands Forensic Institute NFI | Buckleton J.,Environmental Science and Research | Champod C.,University of Lausanne | Evett I.W.,London Laboratory | Jackson G.,University of Abertay Dundee
Science and Justice | Year: 2011

This is a discussion of a number of issues that arise from the recent judgment in R v T [1]. Although the judgment concerned with footwear evidence, more general remarks have implications for all disciplines within forensic science. Our concern is that the judgment will be interpreted as being in opposition to the principles of logical interpretation of evidence. We re-iterate those principles and then discuss several extracts from the judgment that may be potentially harmful to the future of forensic science. A position statement with regard to evidence evaluation, signed by many forensic scientists, statisticians and lawyers, has appeared in this journal [2] and the present paper expands on the points made in that statement. © 2011 Forensic Science Society.

Slooten K.,Netherlands Forensic Institute NFI | Slooten K.,VU University Amsterdam
Forensic Science International: Genetics | Year: 2016

While likelihood ratio calculations were until the recent past limited to the evaluation of mixtures in which all alleles of all donors are present in the DNA mixture profile, more recent methods are able to deal with allelic dropout and drop-in. This opens up the possibility to obtain likelihood ratios for mixtures where this was not previously possible, but it also means that a full match between the alleged contributor and the crime stain is no longer necessary. We investigate in this article what the consequences are for relatives of the actual donors, because they typically share more alleles with the true donor than an unrelated individual. We do this with a semi-continuous binary approach, where the likelihood ratios are based on the observed alleles and the dropout probabilities for each donor, but not on the peak heights themselves. These models are widespread in the forensic community. Since in many cases a simple model is used where a uniform dropout probability is assumed for all (or for all unknown) contributors, we explore the extent to which this alters the false positive probabilities for relatives of donors, compared to what would have been obtained with the correct probabilities of dropout for each donor. © 2015 Elsevier Ireland Ltd. All rights reserved.

Muramoto S.,U.S. National Institute of Standards and Technology | Forbes T.P.,U.S. National Institute of Standards and Technology | Van Asten A.C.,Netherlands Forensic Institute NFI | Van Asten A.C.,University of Amsterdam | Gillen G.,U.S. National Institute of Standards and Technology
Analytical Chemistry | Year: 2015

A novel test sample for the spatially resolved quantification of illicit drugs on the surface of a fingerprint using time-of-flight secondary ion mass spectrometry (ToF-SIMS) and desorption electrospray ionization mass spectrometry (DESI-MS) was demonstrated. Calibration curves relating the signal intensity to the amount of drug deposited on the surface were generated from inkjet-printed arrays of cocaine, methamphetamine, and heroin with a deposited-mass ranging nominally from 10 pg to 50 ng per spot. These curves were used to construct concentration maps that visualized the spatial distribution of the drugs on top of a fingerprint, as well as being able to quantify the amount of drugs in a given area within the map. For the drugs on the fingerprint on silicon, ToF-SIMS showed great success, as it was able to generate concentration maps of all three drugs. On the fingerprint on paper, only the concentration map of cocaine could be constructed using ToF-SIMS and DESI-MS, as the signals of methamphetamine and heroin were completely suppressed by matrix and substrate effects. Spatially resolved quantification of illicit drugs using imaging mass spectrometry is possible, but the choice of substrates could significantly affect the results. © 2015 American Chemical Society.

Ricciardi F.,University of Florence | Slooten K.,Netherlands Forensic Institute NFI
Forensic Science International: Genetics | Year: 2014

In recent years, the use of DNA data for personal identification has become a crucial feature for forensic applications such as disaster victim identification (DVI). Computational methods to cope with these kinds of problems must be designed to handle large scale events with a high number of victims, obtaining likelihood ratios and posterior odds with respect to different identification hypotheses. Trying to minimize identification error rates (i.e., false negatives and false positives), a number of computational methods, based either on the choice between alternative mutation models or on the adoption of a different strategy, are proposed and evaluated. Using simulation of DNA profiles, our goal is to suggest which is the most appropriate way to address likelihood ratio computation in DVI cases, especially to be able to efficiently deal with complicating issues such as mutations or null alleles, considering that data about these latter are limited and fragmentary. © 2014 Elsevier Ireland Ltd.

Bailey J.A.,Minnesota State University, Mankato | Wang Y.,University of North Carolina at Wilmington | van de Goot F.R.W.,VU University Amsterdam | Gerretsen R.R.R.,Netherlands Forensic Institute NFI | Gerretsen R.R.R.,Leiden University
Forensic Science, Medicine, and Pathology | Year: 2011

Saw marks on bone have been routinely reported in dismemberment cases. When saw blade teeth contact bone and the bone is not completely sawed into two parts, bone fragments are removed forming a channel or kerf. Therefore, kerf width can approximate the thickness of the saw blade. The purpose of this study is to evaluate 100 saw kerf widths in bone produced by ten saw types to determine if a saw can be eliminated based on the kerf width. Five measurements were taken from each of the 100 saw kerfs to establish an average thickness for each kerf mark. Ten cuts were made on 10 sections of bovine bone, five with human-powered saws and five with mechanical-powered saws. The cuts were examined with a stereoscopic microscope utilizing digital camera measuring software. Two statistical cumulative logistic regression models were used to analyze the saw kerf data collected. In order to estimate the prediction error, repeated stratified cross-validation was applied in analyzing the kerf mark data. Based on the two statistical models used, 70-90% of the saws could be eliminated based on kerf width. © 2010 The Author(s).

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