The discrimination of ignitable liquids and ignitable liquid residues using chemometric analysis

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Awarding institution
  • University of Strathclyde
Date of award
  • 2012
Thesis identifier
  • T13021
Qualification Level
Qualification Name
Department, School or Faculty
  • 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.
Resource Type
Date Created
  • 2012
Former identifier
  • 941808