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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.
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
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This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction.
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.