During crime scene investigations, numerous traces are secured and may be used as evidence for the evaluation of source and/or activity level propositions. The rapid chemical analysis of a biological trace enables the identification of body fluids and can provide significant donor profiling information, including age, sex, drug abuse, and lifestyle. Such information can be used to provide new leads, exclude from, or restrict the list of possible suspects during the investigative phase. This paper reviews the state-of-the-art labelling techniques to identify the most suitable visual enhancer to be implemented in a lateral flow immunoassay setup for the purpose of trace identification and/or donor profiling. Upon comparison, and with reference to the strengths and limitations of each label, the simplistic one-step analysis of noncompetitive lateral flow immunoassay (LFA) together with the implementation of carbon nanoparticles (CNPs) as visual enhancers is proposed for a sensitive, accurate, and reproducible in situ trace analysis. This approach is versatile and stable over different environmental conditions and external stimuli. The findings of the present comparative analysis may have important implications for future forensic practice. The selection of an appropriate enhancer is crucial for a well-designed LFA that can be implemented at the crime scene for a time- and cost-efficient investigation.
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E-discovery projects typically start with an assessment of the collected electronic data in order to estimate the risk to prosecute or defend a legal case. This is not a review task but is appropriately called early case assessment, which is better known as exploratory search in the information retrieval community. This paper first describes text mining methodologies that can be used for enhancing exploratory search. Based on these ideas we present a semantic search dashboard that includes entities that are relevant to investigators such as who knew who, what, where and when. We describe how this dashboard can be powered by results from our ongoing research in the “Semantic Search for E-Discovery” project on topic detection and clustering, semantic enrichment of user profiles, email recipient recommendation, expert finding and identity extraction from digital forensic evidence.
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Fingerprints are widely used in forensic science for individualization purposes. However, not every fingermark found at a crime scene is suitable for comparison, for instance due to distortion of ridge detail, or when the reference fingerprint is not in the database. To still retrieve information from these fingermarks, several studies have been initiated into the chemical composition of fingermarks, which is believed to be influenced by several donor traits. Yet, it is still unclear what donor information can be retrieved from the composition of one's fingerprint, mainly because of limited sample sizes and the focus on analytical method development. It this paper, we analyzed the chemical composition of 1852 fingerprints, donated by 463 donors during the Dutch music festival Lowlands in 2016. In a targeted approach we compared amino acid and lipid profiles obtained from different types of fingerprints. We found a large inter-variability in both amino acid and lipid content, and significant differences in L-(iso)leucine, L-phenylalanine and palmitoleic acid levels between male and female donors. In an untargeted approach we used full-scan MS data to generate classification models to predict gender (77.9% accuracy) and smoking habit (90.4% accuracy) of fingerprint donors. In the latter, putatively, nicotine and cotinine are used as predictors.
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