Identifying Ignitable Liquids In Fire Debris

Identifying Ignitable Liquids in Fire Debris: A Guideline for Forensic Experts discusses and illustrates the characteristics of different ignitable liquid products. This guideline builds on the minimum criteria of the ignitable liquid classes defined in the internationally accepted standard ASTM E1618 Standard Test Method for Ignitable Liquid Residues in Extracts from Fire Debris Samples by Gas Chromatography-Mass Spectrometry. The volume provides information on the origin of the characteristics of these ignitable liquid products and provides a summary of characteristics to demonstrate a positive identification of the particular product class. Topics such as the term ignitable liquid, relevant guidelines for fire debris analysis, production processes of ignitable liquids, fire debris analysis methods, and interferences in fire debris analysis, are briefly discussed as these topics are essential for the understanding of the identification and classification of ignitable liquid residues in fire debris. Discusses the characteristics and variations in chemical composition of different classes of the ignitable liquid products defined by ASTM E1618:14 Covers the General Production Processes of Ignitable Liquid Products Includes a guide for the Identification of Ignitable Liquids in Fire Debris

The study of fire debris analysis is vital to the function of all fire investigations, and, as such, Fire Debris Analysis is an essential resource for fire investigators. The present methods of analysis include the use of gas chromatography and gas chromatography-mass spectrometry, techniques which are well established and used by crime laboratories throughout the world. However, despite their universality, this is the first comprehensive resource that addresses their application to fire debris analysis. Fire Debris Analysis covers topics such as the physics and chemistry of fire and liquid fuels, the interpretation of data obtained from fire debris, and the future of the subject. Its cutting-edge material and experienced author team distinguishes this book as a quality reference that should be on the shelves of all crime laboratories. Serves as a comprehensive guide to the science of fire debris analysis Presents both basic and advanced concepts in an easily readable, logical sequence Includes a full-color insert with figures that illustrate key concepts discussed in the text

Current fire debris analysis procedure involves using the chromatographic patterns of total ion chromatograms, extracted ion chromatograms, and target compound analysis to identify an ignitable liquid according to the American Society for Testing and Materials (ASTM) E 1618 standard method. Classifying the ignitable liquid is accomplished by a visual comparison of chromatographic data obtained from any extracted ignitable liquid residue in the debris to the chromatograms of ignitable liquids in a database, i.e. by visual pattern recognition. Pattern recognition proves time consuming and introduces potential for human error. One particularly difficult aspect of fire debris analysis is recognizing an ignitable liquid residue when the intensity of its chromatographic pattern is extremely low or masked by pyrolysis products. In this research, a unique approach to fire debris analysis was applied by utilizing the samples' total ion spectrum (TIS) to identify an ignitable liquid, if present. The TIS, created by summing the intensity of each ion across all elution times in a gas chromatography-mass spectrometry (GC-MS) dataset retains sufficient information content for the identification of complex mixtures . Computer assisted spectral comparison was then performed on the samples' TIS by target factor analysis (TFA). This approach allowed rapid automated searching against a library of ignitable liquid summed ion spectra. Receiver operating characteristic (ROC) curves measured how well TFA identified ignitable liquids in the database that were of the same ASTM classification as the ignitable liquid in fire debris samples, as depicted in their corresponding area under the ROC curve. This study incorporated statistical analysis to aid in classification of an ignitable liquid, therefore alleviating interpretive error inherent in visual pattern recognition. This method could allow an analyst to declare an ignitable liquid present when utilization of visual pattern recognition alone is not sufficient.

Identification of ignitable liquid residues in the presence of background interferences, especially those arising from pyrolysis processes, is a major challenge for the fire debris analyst. The proposed research will lead to a mathematical model that allows for the detection of an ignitable liquid in a fire debris sample and the classification of the ignitable liquid according to the ASTM E1618 classification scheme. The research will examine the influence of substrate pyrolysis and non-pyrolysis interferences on: (1) probability of correct prediction of the presence of an ignitable liquid in real and simulated fire debris samples (Type I and Type II error rates) and (2) probability of correct prediction of the associated ignitable liquid ASTM class and sub-class (heavy, medium or light) in positive samples. Potential alternative sub-groupings of ignitable liquids will be examined based on cluster analysis techniques. Models will be examined which are based on principal components analysis (PCA), linear discriminant analysis (LDA) and soft independent model classification analogy (SIMCA). The model will be developed from the summed ion spectra of nearly 500 ignitable liquid and 50 pyrolysis sample GC-MS data sets with ANOVA-assisted variable selection. Training data sets will be taken from the National Center for Forensic Science ignitable liquid and substrate pyrolysis databases. Simulated fire debris samples generated in the laboratory and samples from large-scale burns will also be employed in model testing. Model performance will be statistically evaluated by receiver operator characteristic analysis. The final model will be implemented in a software solution for forensic laboratory use. This project proposed to investigate the development of a method for classifying fire debris GC-MS data sets as: (1) containing or not containing an ignitable liquid, (2) classifying any ignitable liquid that may be present under the ASTM E1618 classification scheme and (3) estimating the statistical certainty of the answers to questions 1 and 2. The proposed approach is to build a mathematical model that can correctly classify GC-MS data from ignitable liquids and pyrolyzed substrates (wood, plastic, etc.). The model will then be applied to GC-MS data from laboratory-generated fire debris samples, as well as ignitable liquids and substrates that were not used to build the model. The classification success of the model will allow a determination of the statistical performance of the model by ROC analysis. The model will be developed based on the total ion spectrum, which has already shown a propensity for classifying a set of ignitable liquids drawn from multiple ASTM classes.

This text provides training on the fundamental tools and methodologies used in active forensic laboratories for the complicated analysis of fire debris and explosives evidence. It is intended to serve as a gateway for students and transitioning forensic science or chemistry professionals. The book is divided between the two disciplines of fire debris and explosives, with a final pair of chapters devoted to the interplay between the two disciplines and with other disciplines, such as DNA and fingerprint analysis. It brings together a multi-national group of technical experts, ranging from academic researchers to active practitioners, including members of some of the premier forensic agencies of the world. Readers will gain knowledge of practical methods of analysis and will develop a strong foundation for laboratory work in forensic chemistry. End-of-chapter questions based on relevant topics and real-world data provide a realistic arena for learners to test newly-acquired techniques.

The fire debris analyst is often faced with the complex problem of identifying ignitable liquid residues in the presence of products produced from pyrolysis and incomplete combustion of common building and furnishing materials. The purpose of this research is to investigate a modified destructive distillation methodology provided by the Florida Bureau of Forensic Fire and Explosive Analysis to produce interfering product chromatographic patterns similar to those observed in fire debris case work. The volatile products generated during heating of substrate materials are extracted from the fire debris by passive headspace adsorption and subsequently analyzed by GC-MS. Low density polyethylene (LDPE) is utilized to optimize the modified destructive distillation method to produce the interfering products commonly seen in fire debris. The substrates examined in this research include flooring and construction materials along with a variety of materials commonly analyzed by fire debris analysts. These substrates are also burned in the presence of a variety of ignitable liquids. Comparisons of ignitable liquids, pyrolysis products, and products from pyrolysis in the presence of an ignitable liquid are performed by comparing the summed ion spectra from the GC-MS data. Pearson correlation was used to determine if substrates could be discriminated from one another. A pyrolysis products database and GC-MS database software based on comparison of summed ion spectra are shown to be useful tools for the evaluation of fire debris.

The rapidly increasing number of different ignitable liquid formulations available today poses a new challenge to fire debris analysts and other forensic chemistry specialists - that of accurately identifying and classifying ignitable liquids with unfamiliar chromatographic patterns. GC-MS Guide to Ignitable Liquids addresses that challenge with a selection of more than 100 different ignitable liquid formulations designed to supplement the laboratory's standard collection. Both total ion chromatograms and extracted ion chromatograms (mass chromatograms) are included. Written by authors who are also experienced forensic chemists, this complete reference is the only single source of information on ignitable liquids - a must for students of fire science, forensic chemists, and anyone conducting fire debris analysis.

The application of comprehensive two-dimensional gas chromatography (GC × GC) for the forensic analysis of ignitable liquids in fire debris is reported. GC × GC is a high resolution, multidimensional gas chromatographic method in which each component of a complex mixture is subjected to two independent chromatographic separations. The high resolving power of GC × GC can separate hundreds of chemical components from a complex fire debris extract. The GC × GC chromatogram is a multicolor plot of two-dimensional retention time and detector signal intensity that is well suited for rapid identification and fingerprinting of ignitable liquids. GC × GC chromatograms were used to identify and classify ignitable liquids, detect minor differences between similar ignitable liquids, track the chemical changes associated with weathering, characterize the chemical composition of fire debris pyrolysates, and detect weathered ignitable liquids against a background of fire debris pyrolysates.

The goal of the research conducted under this grant was to develop a chemometric method of data analysis that would facilitate the identification of GC-MS patterns associated with ignitable liquid classes, as designated under ASTM E 1618-10. The objective of the research was to develop a data analysis method that would classify ignitable liquid residue in the presence of background interferences found in fire debris. Pattern recognition and classification methods available at the onset of this research did not explicitly take into account background interference issues. A novel method was developed under this research to classify ignitable liquid residues into the ASTM classes, even in the presence of a strong background signal, without a priori knowledge of the background signature. The method makes use of target factor analysis (TFA) in combination with Bayesian decision theory. The use of Bayesian decision theory provides results in the form of posterior probabilities that a set of samples from a fire scene contain an ignitable liquid of a specific ASTM class. Error rates are not currently available for fire debris analysis, other than extrapolations from proficiency tests. The method was further refined by introducing a sensitivity parameter which made the method very conservative in its predictions, and gave a true "soft" classifier. Soft classifiers allow classification of a sample into multiple classes and afford the possibility of not assigning the sample to any of the available classes. In order to achieve the goals, this work was broken down into three tasks.

Recent developments in analytical instrumentation have had an enormous influence on forensic analysis. The mass spectrometer is now an integral part of every forensic laboratory, resulting in greater analytical accuracy, more reliable identification, and lower detection limits. As the instrumental method of choice among forensic analysts, the mass

It is rare to find a book published for forensic examiners who perfoim fire debris analysis. This book addresses two issues for those involved in the identification of ignitable liquids recovered from fire debris; first, the increased availability of "speciality solvents" which do not fall into the five main categories of more familiar and easily recognizable ignitable liquids and, second, the increased use of mass spectrometry for the identification of ignitable liquids.

Within modern forensic science and criminal investigation, experts face several challenges including managing huge amounts of data, handling miniscule pieces of evidence in a chaotic and complex environment, navigating traditional laboratory structures, and, sometimes, dealing with insufficient knowledge. These challenges must be overcome to avoid failure in investigation or miscarriage of justice. Technologies to Advance Automation in Forensic Science and Criminal Investigation provides a platform for researchers to present state-of-the-art technologies within forensic science and criminal investigation. Covering topics such as financial fraud, machine learning, and source camera identification, this book is an essential reference for criminal investigators, justice departments, law enforcement, legislators, computer scientists, automation professionals, researchers, academicians, and students and educators in higher education.

Arson is one of the most challenging crimes for forensic scientists to investigate. The variability in the composition of ignitable liquids, including changes in chemical composition during and after the fire, and the presence of pyrolysis products generated from burning substrates yields a very complex mixture of volatile compounds in samples of fire debris. Headspace extraction of debris samples followed by gas chromatography-mass spectrometry (GC-MS) is the most common approach for fire investigation. For many laboratories, data interpretation is the bottleneck in the workflow, consuming an inordinate amount of analyst time. It is also a process that is highly dependent on the experience and skill of analysts which gives rise to subjective results. Chemometrics offers an alternative to manual data interpretation. However, for this work to be applicable in real-world fire investigations, the chemometric model must be able to classify all major classes of ignitable liquids that can be possibly found in a fire. Construction of a chemometric model requires abundant casework data. This is this not a problem for gasoline, which is the most commonly used ignitable liquid, but it is a challenge for other ILs. The lengthy time needed for the collection of casework debris containing other ILs for the model construction limits the practical use of this work. Therefore, it would be a great benefit if models applicable to casework samples could be generated based on simulated debris profiles. An established debris simulation protocol has been shown to be effective in generating realistic debris for training human analysts. This thesis evaluates the applicability of this simulation protocol for generating debris that are chemometrically identical to casework debris. It was discovered that models trained on the simulated debris were not applicable to casework samples without a significant loss in the accuracy of the model. It was established that the reason for the inadequacy of the simulated debris was that it did not contain sufficient C2-alkyl benzenes and non-aromatic hydrocarbons. Consequently these features which are not characteristic of gasoline were selected by the chemometric model and model quality degraded for real samples. Thus research turned to a study of the effects of temperature on the pyrolysis of household materials, mainly flooring and roofing materials, at temperatures above 400 °C. I was particularly interested in finding conditions that will generate additional BTEX and aliphatic hydrocarbons, which were generally lacking in debris pyrolyzed at 400 °C with the established simulation method.

Current methods in ignitable liquid identification and classification from fire debris rely on pattern recognition of ignitable liquids in total ion chromatograms, extracted ion profiles, and target compound comparisons, as described in American Standards for Testing and Materials E1618-10. The total ion spectra method takes advantage of the reproducibility among sample spectra from the same American Society for Testing and Materials class. It is a method that is independent of the chromatographic conditions that affect retention times of target compounds, thus aiding in the use of computer-based library searching techniques. The total ion spectrum was obtained by summing the ion intensities across all retention times. The total ion spectrum from multiple fire debris samples were combined for target factor analysis. Principal components analysis allowed the dimensions of the data matrix to be reduced prior to target factor analysis, and the number of principal components retained was based on the determination of rank by median absolute deviation. The latent variables were rotated to find new vectors (resultant vectors) that were the best possible match to spectra in a reference library of over 450 ignitable liquid spectra (test factors). The Pearson correlation between target factors and resultant vectors were used to rank the ignitable liquids in the library. Ignitable liquids with the highest correlation represented possible contributions to the sample. Posterior probabilities for the ASTM ignitable liquid classes were calculated based on the probability distribution function of the correlation values. The ASTM ignitable liquid class present in the sample set was identified based on the class with the highest posterior probability value. Tests included computer simulations of artificially generated total ion spectra from a combination of ignitable liquid and substrate spectra, as well as large scale burns in 20'x8'x8' containers complete with furnishings and flooring. Computer simulations were performed for each ASTM ignitable liquid class across a range of parameters. Of the total number of total ion spectra in a data set, the percentage of samples containing an ignitable liquid was varied, as well as the percent of ignitable liquid contribution in a given total ion spectrum. Target factor analysis was them performed on the computer-generated sample set. The correlation values from target factor analysis were used to calculate posterior probabilities for each ASTM ignitable liquid class. Large scale burns were designed to test the detection capabilities of the chemometric approach to ignitable liquid detection under conditions similar to those of a structure fire. Burn conditions were controlled by adjusting the type and volume of ignitable liquid used, the fuel load, ventilation, and the elapsed time of the burn. Samples collected from the large scale burns were analyzed using passive headspace adsorption with activated charcoal strips and carbon disulfide desorption of volatiles for analysis using gas chromatography-mass spectrometry.

Fire Investigation covers the concepts and theories used to determine a specfic fire has been deliberately or accidentally set. The author clearly explains the concepts needed to gain insight into a fire scene investigation, including the dynamics of the fire, the necessary conditions for a fire to start and be maintained, the different types of co