Literature DB >> 30553191

Evaluation of an untargeted chemometric approach for the source inference of ignitable liquids in forensic science.

Miguel de Figueiredo1, Christophe B Y Cordella2, Delphine Jouan-Rimbaud Bouveresse3, Xavier Archer4, Jean-Marc Bégué4, Douglas N Rutledge5.   

Abstract

Recent research efforts in the domain of fire debris analysis have been mainly oriented towards the development of innovative analytical procedures and chemometric approaches for the detection and classification of ignitable liquids in fire specimens according to the ASTM E1618. However, less attention has been brought to the question of the source inference of ignitable liquids. Infer the identity of source of ignitable liquids recovered from arson sites is still a challenging and ongoing research area. In this study, the objective is to link neat gasoline samples sharing a common source through the use of an untargeted chemometric approach applied to data acquired by automated thermodesorption (ATD)-GC-MS following passive headspace extraction onto Tenax TA tubes. To that end, 190 unique gasoline samples from 19 gas stations collected over a year were used. A general and automated chemometric methodology for data treatment involving the following main steps is proposed: feature detection, normalization by exhaustive calculation of ratios between areas of pairs of features and selection of most discriminant ratios. The ratio selection procedure used here is based on the calculation of similarity measurements between pairs of samples sharing a common source or not. The algorithm maximizes the separation of the distributions of similarity measurements for related and unrelated samples by selecting a subset of ratios maximizing the area under the Receiver Operating Characteristics curve. The approach presented here was successfully applied to neat gasoline samples in order to assess if two gasoline samples share a common source or not.
Copyright © 2018 Elsevier B.V. All rights reserved.

Keywords:  Chemometrics; Fire debris analysis; Gasoline; Ignitable liquids; Source inference

Year:  2018        PMID: 30553191     DOI: 10.1016/j.forsciint.2018.11.016

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  2 in total

1.  Artificial intelligence and thermodynamics help solving arson cases.

Authors:  Sander Korver; Eva Schouten; Othonas A Moultos; Peter Vergeer; Michiel M P Grutters; Leo J C Peschier; Thijs J H Vlugt; Mahinder Ramdin
Journal:  Sci Rep       Date:  2020-11-25       Impact factor: 4.379

Review 2.  Interpol review of fire investigation 2016-2019.

Authors:  Éric Stauffer
Journal:  Forensic Sci Int       Date:  2020-03-10       Impact factor: 2.395

  2 in total

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