A variety of lighter fuel samples from different manufacturers (both unevaporated and evaporated) were analyzed using conventional gas chromatography-mass spectrometry (GC-MS) analysis. In total 51 characteristic peaks were selected as variables and subjected to data preprocessing prior to subsequent analysis using unsupervised chemometric analysis (PCA and HCA) and a SOFM artificial neural network. The results obtained revealed that SOFM acted as a powerful means of evaluating and linking degraded ignitable liquid sample data to their parent unevaporated liquids.
This study investigates the optimisation of peroxidase based enhancement techniques for footwear impressions made in blood on various fabric surfaces. Four different haem reagents: leuco crystal violet (LCV), leuco malachite green (LMG), fluorescein and luminol were used to enhance the blood contaminated impressions. The enhancement techniques in this study were used successfully to enhance the impressions in blood on light coloured surfaces, however, only fluorescent and/or chemiluminescent techniques allowed visualisation on dark coloured fabrics, denim and leather. Luminol was the only technique to enhance footwear impressions made in blood on all the fabrics investigated in this study.
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