Sari, Nurul Lizawani
(2020)
Performance evaluation of activated carbon cloth for fire debris analysis with artificial neural network approach.
Masters thesis, Universiti Sains Malaysia.
Abstract
Forensic fire investigates the origin and cause of the fire. Detection and
identification of ignitable liquid (IL) residue in fire debris may provide the vital clue
of the fire cause, especially important to prove incendiary fires. The scope of this study
is narrowed to the usage of activated carbon cloth (ACC) as the adsorbent material by
passive headspace diffusion as the extraction technique of IL residue. This study
investigates the detection of selected target compounds that represent wide IL residue
range from the lightest compound of n-hexane (C6) to the heavier compound of
eicosane (C20) at different extraction parameters especially the temperature setting
(60 °C to 120 °C) and exposure period (2 hours to 24 hours). Data sets from the
chromatographic pattern vary significantly with different parameters were chosen.
Computational modelling of artificial neural network (ANN) based on the pattern was
developed and utilised to evaluate the extraction performance of ACC for optimisation
purposed. The resolution of chromatographic behaviour of 14 selected target
compounds that represent the ignitable liquid was used as input for the ANN model.
The ANN display a response model of (2:2-17-14:14) allows the optimum condition
with the practical setting to be 4 hours at 100 °C for urgent sampling while 18 hours
at 80 °C is intended for overnight sampling. Selected optimum condition and practical
settings for effective extraction of volatile compounds are important knowledge to
facilitate busy laboratory operation as well as the identification and interpretation of
complex fire debris samples. Thus, the finding of this research has a relevant
implication for the forensic analyst who performed fire evidence investigation.
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