Machine learning approaches over ion mobility spectra for the discrimination of ignitable liquids residues from interfering substrates
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SourceTalanta Open, Vol. 6
In arson fires, ignitable liquids (ILs) are frequently used to start combustion. For this reason, detecting IL residues (ILRs) at the fire scene is a key factor in fire investigation to determine whether a crime has been committed as well as to establish the modus operandi of the perpetrator. In the present study, the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) for the detection of ILRs in fire debris from complex matrices in combination with machine learning (ML) tools is proposed as an alternative to the traditional method, based on gas chromatography–mass spectrometry (GC-MS), described by the ASTM E1618 standard method. For this purpose, petroleum-derived substrates (vinyl, nylon, and polyester) and natural substrates (cotton, cork and linoleum) burned alone and with different ILs (gasoline, diesel, ethanol and charcoal starter with kerosene) were used. In addition, samples were taken at different times (0, 1, 6, 12, 24 and 48 h) after the fire was finished. The ion mobility sum spectrum (IMSS) of each sample was obtained and different ML algorithms were applied. The first derivative was performed at the IMSS, as well as a Savitzky-Golay filter. Hierarchical cluster analysis (HCA) revealed a clustering trend as a function of substrate and ILs used, where the studied sampling times did not affect the resulting clusters. The classification models for the detection of the presence of ILRs have high performance with an accuracy of 100% for support vector machines (SVM) and random forest model (RF), followed by linear discriminant analysis (LDA) with an accuracy of 86.67%. When discriminating the type of ILs used, the RF model obtained an accuracy of 100%, followed by the LDA with 97.22% and finally the SVM model with an accuracy of 93.06%. In addition, a simple web application has been developed where the trained models can be used, so any researcher can apply the method to detect ILRs in fire debris.
SubjectsMachine learning algorithms; Fire debris; Ignitable liquids; Ion mobility spectrometry; Petroleum-derived substrates
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