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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.
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.
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.
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.
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.
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.
Chemometrics, or the application of multivariate statistics to chemical data, provides informative and statistically valid analyses within a forensic context and there has been an increase in the use of chemometrics to characterise forensic exhibits. Introducing chemometric methods suitable for forensic practitioners, this book fills a gap in the literature outlining how such methods are applied to forensic casework, what limitations to these approaches exist, and future trends emerging in the field. The book highlights how chemometric methods may be applied to different areas of forensic science, enabling more confident and transparent decision-making based on quantitative approaches. It is divided into various sections which include a background to chemometrics, types of chemometric methods, their applications in various disciplines of forensic science, and emerging trends in the field. The detailed discussion of chemometric methods used for the examination of forensic exhibits outlines their advantages, limitations, and efficiency. Providing a centralised source of information addressing the above aspects, and suitable for forensic practitioners, researchers and stakeholders, this book is written for MSc Forensic Science courses and more broadly applications in the biological, chemical and physical sciences.