Chemometric classification of casework arson samples based on gasoline content.

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.

Preparation of pyrolysis reference samples: evaluation of a standard method using a tube furnace.

A new, simple method for the reproducible creation of pyrolysis products from different materials that may be found at a fire scene is described. A temperature programmable steady-state tube furnace was used to generate pyrolysis products from different substrates, including softwoods, paper, vinyl sheet flooring, and carpet. The temperature profile of the tube furnace was characterized, and the suitability of the method to reproducibly create pyrolysates similar to those found in real fire debris was assessed. The use of this method to create proficiency tests to realistically test an examiner's ability to interpret complex gas chromatograph-mass spectrometric fire debris data, and to create a library of pyrolsates generated from materials commonly found at a fire scene, is demonstrated.

Principal component analysis and analysis of variance on the effects of Entellan New on the Raman spectra of fibers.

During the forensic examination of textile fibers, fibers are usually mounted on glass slides for visual inspection and identification under the microscope. One method that has the capability to accurately identify single textile fibers without subsequent demounting is Raman microspectroscopy. The effect of the mountant Entellan New on the Raman spectra of fibers was investigated to determine if it is suitable for fiber analysis. Raman spectra of synthetic fibers mounted in three different ways were collected and subjected to multivariate analysis. Principal component analysis score plots revealed that while spectra from different fiber classes formed distinct groups, fibers of the same class formed a single group regardless of the mounting method. The spectra of bare fibers and those mounted in Entellan New were found to be statistically indistinguishable by analysis of variance calculations. These results demonstrate that fibers mounted in Entellan New may be identified directly by Raman microspectroscopy without further sample preparation.

Automated optimization and construction of chemometric models based on highly variable raw chromatographic data.

Direct chemometric interpretation of raw chromatographic data (as opposed to integrated peak tables) has been shown to be advantageous in many circumstances. However, this approach presents two significant challenges: data alignment and feature selection. In order to interpret the data, the time axes must be precisely aligned so that the signal from each analyte is recorded at the same coordinates in the data matrix for each and every analyzed sample. Several alignment approaches exist in the literature and they work well when the samples being aligned are reasonably similar. In cases where the background matrix for a series of samples to be modeled is highly variable, the performance of these approaches suffers. Considering the challenge of feature selection, when the raw data are used each signal at each time is viewed as an individual, independent variable; with the data rates of modern chromatographic systems, this generates hundreds of thousands of candidate variables, or tens of millions of candidate variables if multivariate detectors such as mass spectrometers are utilized. Consequently, an automated approach to identify and select appropriate variables for inclusion in a model is desirable. In this research we present an alignment approach that relies on a series of deuterated alkanes which act as retention anchors for an alignment signal, and couple this with an automated feature selection routine based on our novel cluster resolution metric for the construction of a chemometric model. The model system that we use to demonstrate these approaches is a series of simulated arson debris samples analyzed by passive headspace extraction, GC-MS, and interpreted using partial least squares discriminant analysis (PLS-DA).

Characterization of the products formed by the reaction of trichlorocyanuric acid with 2-propanol.

We report a recent investigation into the death of a cat that was initially thought to involve intentionally burning the animal via the use of an ignitable liquid. The exposure of the animal to flame was ruled out. Instead, forensic investigation revealed the intentional mixing together of a common outdoor swimming pool chlorinator, trichlorocyanuric acid (TCCA), and 2-propanol (aka, isopropyl alcohol or rubbing alcohol). The reaction of these two chemicals resulted in the formation of cyanuric acid residue, hydrochloric acid, and the evolution of a significant volume of chlorine gas. Further alpha-chlorination side reactions also occurred between 2-propanol and TCCA to produce a variety of chlorinated 2-propanone species that were detected on the submitted evidence. The identification of the products of both the main reaction and the side reactions allowed the authors to determine what chemicals were originally mixed together by the culprit.

Evaluation of internal standards for the analysis of ignitable liquids in fire debris.

An evaluation of eight compounds for use as an internal standard in fire debris analysis was conducted. Tests were conducted on tetrachloroethylene, chlorobenzene, n-octylbenzene, 3-phenyltolune, and deuterated compounds toluene-d8, styrene-d8, naphthalene-d8, and diphenyl-d10 to measure the extraction efficiency of each compound in the presence of an interfering volatile compound (carbon disulfide). Other tests were conducted to evaluate whether or not the presence of an ignitable liquid or pyrolysis/combustion products from fire debris would interfere with the identification of these compounds when used as an internal standard. The results showed that while any of the eight compounds could be used as an internal standard in fire debris analysis, the more volatile compounds (toluene-d8, tetrachloroethylene, chlorobenzene, and styrene-d8) showed better extraction efficiencies at room temperature than when heated to 60 degrees C. Each of the less volatile compounds (naphthalene-d8, diphenyl-d10, n-octylbenzene, and 3-phenyltolune) performed well during extraction at 60 degrees C, while naphthalene-d8 showed better extraction efficiency in the presence of competing volatiles when extracted at room temperature.

Fire investigation and ignitable liquid residue analysis--a review: 2001-2007.

Next to natural disasters fires cause some of the greatest losses to property and human life around the world. Arson, the deliberate setting of a fire to destroy property or to take a human life, is one of the most difficult crimes to investigate because much of the evidence at the scene is destroyed by the fire. Fortunately, the science of fire investigation is not static and more information to help investigators determine the origin and cause of a fire through careful examination of the scene and laboratory analysis of fire debris is published every year. This review article provides an overview of the scientific literature describing research and best practices in the fields of fire scene investigation as well as ignitable liquid residue analysis. This review is a compilation of articles published between late 2001 and early 2007. Conference proceedings for which full papers have not been published were intentionally excluded from this review. Some of the information contained in this review was presented at the 14th Interpol Forensic Science Symposium held in Lyon, France in October 2004.

Use of a solid absorbent and an accelerant detection canine for the detection of ignitable liquids burned in a structure fire.

Ignitable Liquid Absorbent (ILA), a commercial solid absorbent intended to assist fire scene investigators in sample location and collection, has been field tested in three separate room fires. The ability of the ILA to detect and absorb different amounts of gasoline, odorless paint thinner, and camp fuel on two different substrates after a full-scale burn was assessed against results from an accelerant detection canine and laboratory analysis using gas chromatography-mass spectrometry (GC-MS). The canine correctly alerted on most of the panels that contained an ignitable liquid after the fire, while the ILA indicator dye failed to indicate in the presence of gasoline and camp fuel. GC-MS results for ignitable liquid residue from each panel and from the ILA showed that ILA absorbed odorless paint thinner and camp fuel from most of the test panels, but failed to absorb gasoline from the panels on which gasoline was confirmed to be present.