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Forensic Sci Int. 2014 Feb;235:24-31. doi: 10.1016/j.forsciint.2013.11.014. Epub 2013 Dec 16.

Chemometric classification of casework arson samples based on gasoline content.

Author information

1
Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada.
2
Trace Evidence Services, Royal Canadian Mounted Police, Edmonton, Alberta, Canada.
3
Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada. Electronic address: James.Harynuk@ualberta.ca.

Abstract

Detection and identification of ignitable liquids (ILs) in arson debris is a critical part of arson investigations. The challenge of this task is due to the complex and unpredictable chemical nature of arson debris, which also contains pyrolysis products from the fire. ILs, most commonly gasoline, are complex chemical mixtures containing hundreds of compounds that will be consumed or otherwise weathered by the fire to varying extents depending on factors such as temperature, air flow, the surface on which IL was placed, etc. While methods such as ASTM E-1618 are effective, data interpretation can be a costly bottleneck in the analytical process for some laboratories. In this study, we address this issue through the application of chemometric tools. Prior to the application of chemometric tools such as PLS-DA and SIMCA, issues of chromatographic alignment and variable selection need to be addressed. Here we use an alignment strategy based on a ladder consisting of perdeuterated n-alkanes. Variable selection and model optimization was automated using a hybrid backward elimination (BE) and forward selection (FS) approach guided by the cluster resolution (CR) metric. In this work, we demonstrate the automated construction, optimization, and application of chemometric tools to casework arson data. The resulting PLS-DA and SIMCA classification models, trained with 165 training set samples, have provided classification of 55 validation set samples based on gasoline content with 100% specificity and sensitivity.

KEYWORDS:

Arson; Chemometrics; Cluster resolution; GC–MS; Gasoline; Variable selection

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