Fire Investigation and Ignitable Liquid Residue Analysis

A fire investigation is a difficult and challenging task. An investigator's basic task at a fire scene is two-fold: first, to ascertain the origin of the fire and, second, to closely investigate the site of origin and try to determine what triggered a fire to start at or near that spot. Usually, an investigation would begin by attempting to obtain a general view of the site and the fire damage; this may be achieved at ground level or from an elevated location. Following this, one may examine the materials available, the fuel load, and the condition of the debris at different locations. Surprisingly, the science of fire investigation is not stagnant, and each year, more information to assist investigators in determining the location and cause of a fire by diligent observation of the scene and laboratory study of fire debris is released. This chapter is split into two sections. The first section discusses the general procedures to be used during a fire investigation, and the second section discusses laboratory analysis of ignitable liquid residue analysis.

Activated Charcoal Pellets as an Innovative Method for Forensic Analysis of Ignitable Liquid Residues from Fire Debris by GC-MS

In Forensic Chemistry, evidence collected at a crime scene is of paramount importance for any case to be properly elucidated. Ignitable liquid residues are important chemical evidence in investigations into cases of fire because these substances can be correlated to arson. Here, we describe an innovative technique for sampling and extracting gasoline and diesel from fire debris by using activated charcoal pellets (ACP). ACP can be an alternative to activated charcoal strips and can be easily produced on the laboratory scale. The ACP approach allowed all the target compounds selected for gasoline and diesel fuels to be extracted. Among the six tested extraction conditions, optimal extraction occurred at 100 °C, after 240 min. These preliminary results showed the potential of ACP for detecting gasoline and diesel in fire debris. However, the ACP approach still requires analytical validation, so that its applicability in an authentic forensic setting can be explored.

Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics

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.