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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.
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
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.
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.
Fire debris analysis is a forensic science discipline that determines if an ignitable liquid residue is present or absent in a fire debris sample. Currently, fire debris analysis results in categorical statements based on qualitative data, not the quantitative evidentiary value of data. The purpose of this research was to develop a novel software application to aid fire debris analysts in the identification and classification of ignitable liquid residues that are found in fire debris samples. The developed application uses target factor analysis (TFA) and Pearson correlation for compound identification in gas chromatograms using mass spectral comparison and allows for visual comparison of unknown fire debris samples chromatograms to ignitable liquid references from the National Center for Forensic Science (NCFS) Ignitable Liquid Reference Collection (ILRC). Frequencies of occurrences were calculated for each of 295 compounds from the NCFS compound library through compound identification of ignitable liquid, substrate, and fire debris samples using the novel computer application. The log-likelihood ratios of compounds determined to be within an optimal subset of best chromosomes determined using a genetic algorithm were used for calculating Naïve Bayes log-likelihood ratios for fire debris samples. Finally, self-organizing feature maps (SOFM), trained with in-silico total ion spectra data, were used to classify ground truth fire debris samples into American Society for Testing and Materials (ASTM) E1618-19 classes. Pearson correlation was then used to compare the total ion chromatograms of the classified fire debris samples were then compared to the in-silico total ion chromatograms located within the assigned SOFM node. The performance and validation of these models are discussed further in this dissertation.
Fire debris analysis currently relies on visual pattern recognition of the total ion chromatograms, extracted ion profiles, and target compound chromatograms to identify the presence of an ignitable liquid. This procedure is described in the ASTM International E1618-10 standard method. For large data sets, this methodology can be time consuming and is a subjective method, the accuracy of which is dependent upon the skill and experience of the analyst. This research aimed to develop an automated classification method for large data sets and investigated the use of the total ion spectrum (TIS). The TIS is calculated by taking an average mass spectrum across the entire chromatographic range and has been shown to contain sufficient information content for the identification of ignitable liquids. The TIS of ignitable liquids and substrates were compiled into model data sets. Substrates are defined as common building materials and household furnishings that are typically found at the scene of a fire and are, therefore, present in fire debris samples. Fire debris samples were also used which were obtained from laboratory-scale and large-scale burns. An automated classification method was developed using computational software, that was written in-house. Within this method, a multi-step classification scheme was used to detect ignitable liquid residues in fire debris samples and assign these to the classes defined in ASTM E1618-10. Classifications were made using linear discriminant analysis, quadratic discriminant analysis (QDA), and soft independent modeling of class analogy (SIMCA). The model data sets were tested by cross-validation and used to classify fire debris samples. Correct classification rates were calculated for each data set. Classifier performance metrics were also calculated for the first step of the classification scheme which included false positive rates, true positive rates, and the precision of the method. The first step, which determines a sample to be positive or negative for ignitable liquid residue, is arguably the most important in the forensic application. Overall, the highest correct classification rates were achieved using QDA for the first step of the scheme and SIMCA for the remaining steps. In the first step of the classification scheme, correct classification rates of 95.3% and 89.2% were obtained using QDA to classify the cross-validation test set and fire debris samples, respectively. For this step, the cross-validation test set resulted in a true positive rate of 96.2%, a false positive rate of 9.3%, and a precision of 98.2%. The fire debris data set had a true positive rate of 82.9%, a false positive rate of 1.3%, and a precision of 99.0%. Correct classifications rates of 100% were achieved for both data sets in the majority of the remaining steps which used SIMCA for classification. The lowest correct classification rate, 69.2%, was obtained for the fire debris samples in one of the final steps in the classification scheme. In this research, the first statistically valid error rates for fire debris analysis have been developed through cross-validation of large data sets. The fire debris analyst can use the automated method as a tool for detecting and classifying ignitable liquid residues in fire debris samples. The error rates reduce the subjectivity associated with the current methods and provide a level of confidence in sample classification that does not currently exist in forensic fire debris analysis.
One of the major challenges in fire investigation is the determination of the cause of fire. The fire can be accidental or intentional. The determination of ignitable liquid residue (ILR) from fire debris helps the process and this process is called fire debris analysis in forensic science. This is one of the most complex areas in the field of forensics because of the evaporation of the ILR from the debris and the interferences of the substrate matrix with the ILR if present. In the present, the final decisions in fire debris analysis are based on categorical statements and it only represents the qualitative but not the quantitative value of the data. The likelihood ratio approach is one of the most widely used methods in forensic science in expressing the evidentiary value.
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
Hydrocarbon fuels such as petrol and petroleum distillate products are commonly used to set deliberate fires. In fire debris analysis, characterisation and identification of these accelerants are based on subjective pattern matching to a reference collection or database. Such procedures involving manual comparison, is often hampered by the complex nature of the samples when exposed to heat, especially in the presence of interfering products and can be extremely challenging. The application of chemometrics and Artificial Neural Networks (ANNs) pattern recognition techniques are examined in this work to determine their abilities to objectively match chromatographic profiles derived from evaporated ignitable liquid samples to their un-evaporated source. The abilities of the mathematical methods to further resolve ignitable liquid patterns when in the presence of interfering pyrolysis and combustion products is also investigated. Data pre-treatment via normalisation and power transformation prior mathematical analysis is examined and discussed. Petrol and petroleum distillate products of light, medium and heavy fractions, obtained from a variety of manufacturers, were examined. Their objective classification and discrimination using the mathematical techniques under study is exposed and discussed. The link between evaporated and unevaporated samples was poorly established by conventional chemometric techniques using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). In contrast, Self Organising Feature Maps (SOFM), an ANN technique, provided excellent classification and full discrimination of light and medium petroleum distillate samples by specific brand. Classifications of petrol and diesel samples by brand were less successful. However, some meaningful associations were possible within the petrol groupings using SOFM, and all evaporated samples were correctly associated into the clusters containing their un-evaporated counterparts. In addition, SOFM provided successful and unequivocal discrimination of ignitable liquid residues recovered from fire debris according to the class of ignitable liquid in all samples tested. The findings from this work prompt further exploration on the potential use of SOFM as a mathematical strategy for the objective comparison of ignitable liquids and their residues from fire debris samples.
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.