Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 514464
Title Local Ion Signatures (LIS) for the examination of comprehensive two-dimensional gas chromatography applied to fire debris analysis
Author(s) Lopatka, Martin; Sampat, Andjoe A.; Jonkers, Steffan; Adutwum, Lawrence A.; Mol, Hans G.J.; Weg, Guido van der; Harynuk, James J.; Schoenmakers, Peter J.; Asten, Arian van; Sjerps, Marjan J.; Vivó-Truyols, Gabriel
Source Forensic Chemistry 3 (2017). - ISSN 2468-1709 - p. 1 - 13.
Department(s) BU Contaminants & Toxins
Publication type Refereed Article in a scientific journal
Publication year 2017
Keyword(s) Chemometrics - Comprehensive two-dimensional gas chromatography (GC × GC–MS) - Fire Debris Analysis (FDA) - Likelihood ratio (LR) - Local Ion Signature (LIS)

Forensic examination of fire debris evidence is a notoriously difficult analytical task due to the complexity and variability of sample composition. The use of comprehensive two-dimensional gas chromatography with mass spectrometry detection (GC × GC–MS) allows the coupling of orthogonal retention mechanisms and therefore a high peak capacity. We demonstrate recent innovations in combining chemometric techniques for data reduction and feature selection, with evaluation of the evidence for forensic questions pertaining to the detection and subsequent classification of ignitable liquid residue (ILR) in fire debris samples. Chromatograms are divided into non-overlapping spatially delimited regions; for each of these regions a Local Ion Signature (LIS) is computed by summing the intensities, per nominal mass/charge over all points contained within each region. This yields a reduced feature space representing the original data as a set of consolidated ion traces. Subsequent feature selection is performed by evaluating the individual efficacy of each feature using a univariate score-based likelihood ratio (LR) approach for discriminating between pairs of same or different type samples. The retained features are used to model each ILR class using linear discriminant analysis (LDA). Results are demonstrated for 155 arson samples containing a diversity of substrate compounds and several known ignitable liquids. ILR detection is performed at 84% accuracy with fewer than 1% false positives followed by subsequent classification. Likelihood ratio distributions are presented referring to both detection and classification tasks.

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