Abstract
Interpretation of data from fire debris is considered as one of the most challenging steps in fire investigation. Forensic analysts are tasked to identify the presence or absence of ignitable liquid residues (ILRs) which may indicate whether a fire was started deliberately. So far, data analysis is subjected to human interpretation following the American Society for Testing and Materials’ guidelines (ASTM E1618) based on gas chromatography–mass spectrometry data. However, different factors such as interfering pyrolysis compounds may hinder the interpretation of data. Some substrates release compounds that are in the range of common ignitable liquids, which interferes with accurate determination of ILRs. The aim of the current research is to investigate whether headspace–mass spectroscopy electronic nose (HS-MS eNose) combined with pattern recognition can be used to classify different ILRs from fire debris samples that contain a complex matrix (petroleum-based substrates or synthetic fibers carpet) that can strongly interfere with their identification. Six different substrates—four petroleum-derived substrates (vinyl, linoleum, polyester, and polyamide carpet), as well as two different materials for comparison purposes (cotton and cork) were used to investigate background interferences. Gasoline, diesel, ethanol, and charcoal starter with kerosene were used as ignitable liquids. In addition, fire debris samples were taken after different elapsed times. A total of 360 fire debris samples were analyzed. The obtained total ion mass spectrum was combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) as well as supervised linear discriminant analysis (LDA). The results from HCA show a strong tendency to group the samples according to the ILs and substrate used, and LDA allowed for a full identification and discrimination of every ILR regardless of the substrate.
Highlights
The tendency toto cluster according to the presence/absence of ignitable liquids (ILs), First, the tendencyofofthe thesamples samples cluster according to the presence/absence of the type of or substrate as well as the sampling time was checked
The proposed method allows the detection of ignitable liquid residues (ILRs) in fire
headspace–mass spectroscopy electronic nose (HS-mass spectrum (MS)) eNose combined with hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) was used to develop and validate a debris even when complex matrices are burned
Summary
Proper identification of ignitable liquid residues (ILRs) in fire debris is complex in itself since the remaining samples after a fire contain substrate background compounds as well as other products from combustion and pyrolysis processes. For this reason, one of the challenges for forensic analysts consists of isolating ILR’s target compounds from either background or pyrolysis compounds that may interfere with the analysis and obstruct proper identification of the target compounds [1,2]. The most common method for the separation of ILRs in fire debris samples is based on headspace analysis using activated carbon strips as 4.0/). The analysis of the static or dynamic headspace without any adsorbent has been applied [7,8]
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