Effects of Fire Suppression Agents and Weathering in the Analysis of Fire Debris by HS-MS eNose

In arson attacks the detection of ignitable liquid residues (ILRs) at fire scenes provides key evidence since ignitable liquids, such as gasoline, are commonly used to initiate the fire. In most forensic laboratories gas chromatography-mass spectrometry is employed for the analysis of ILRs. When a fire occurs, suppression agents are used to extinguish the fire and, before the scene is investigated, the samples at the scene are subjected to a variety of processes such as weathering, which can significantly modify the chemical composition and thus lead to erroneous conclusions. In order to avoid this possibility, the application of chemometric tools that help the analyst to extract useful information from data is very advantageous. The study described here concerned the application of a headspace-mass spectrometry electronic nose (HS-MS eNose) combined with chemometric tools to determine the presence/absence of gasoline in weathered fire debris samples. The effect of applying two suppression agents (Cafoam Aquafoam AF-6 and Pyro-chem PK-80 Powder) and delays in the sampling time (from 0 to 48 h) were studied. It was found that, although the suppression systems affect the mass spectra, the HS-MS eNose in combination with suitable pattern recognition chemometric tools, such as linear discriminant analysis, is able to identify the presence of gasoline in any of the studied situations (100% correct classification).

[1]  C. García-Ruiz,et al.  Analytical tools for the analysis of fire debris. A review: 2008-2015. , 2016, Analytica chimica acta.

[2]  C. García-Ruiz,et al.  Study of chemical modifications in acidified ignitable liquids analysed by GC-MS. , 2015, Science & justice : journal of the Forensic Science Society.

[3]  J. K. Hardy,et al.  Accelerant classification by gas chromatography/mass spectrometry and multivariate pattern recognition , 2000 .

[4]  Marta Ferreiro-González,et al.  Characterization and Differentiation of Petroleum-Derived Products by E-Nose Fingerprints , 2017, Sensors.

[5]  José Luis Pérez Pavón,et al.  Strategies for qualitative and quantitative analyses with mass spectrometry-based electronic noses , 2006 .

[6]  Eric Stauffer,et al.  ASTM standards for fire debris analysis: a review. , 2003, Forensic science international.

[7]  Bailey E. Knowlton The Effects of Using Fire-Fighting Foams: GC-MS Pattern Analysis of Fire Debris , 2012 .

[8]  Philip Doble,et al.  Classification of premium and regular gasoline by gas chromatography/mass spectrometry, principal component analysis and artificial neural networks. , 2003, Forensic science international.

[10]  M. Ferreiro-González,et al.  Characterization of petroleum-based products in water samples by HS-MS , 2018, Fuel.

[11]  C. Lennard,et al.  A GC–MS database of target compound chromatograms for the identification of arson accelerants , 1995 .

[12]  E. McGee,et al.  A study of the effects of a Micelle Encapsulator Fire Suppression Agent on dynamic headspace analysis of fire debris samples. , 2002, Journal of forensic sciences.

[13]  Michael E. Sigman,et al.  Ignitable Liquid Classification and Identification Using the Summed-Ion Mass Spectrum , 2008 .

[14]  Rosa Baby,et al.  Quality control of medicinal plants with an electronic nose , 2005 .

[15]  G. Zadora,et al.  Application of Head-Space Analysis with Passive Adsorption for Forensic Purposes in the Automated Thermal Desorption-Gas Chromatography-Mass Spectrometry System , 2004 .

[16]  Marta Ferreiro-González,et al.  Determination of Ignitable Liquids in Fire Debris: Direct Analysis by Electronic Nose , 2016, Sensors.

[17]  Mary R. Williams,et al.  Combined target factor analysis and Bayesian soft-classification of interference-contaminated samples: forensic fire debris analysis. , 2012, Forensic science international.

[19]  M. Ferreiro-González,et al.  New Headspace-Mass Spectrometry Method for the Discrimination of Commercial Gasoline Samples with Different Research Octane Numbers , 2014 .

[20]  Dehan Luo,et al.  Application of ANN with extracted parameters from an electronic nose in cigarette brand identification , 2004 .

[21]  E. du Pasquier,et al.  Chemical fingerprinting of gasoline. 2. Comparison of unevaporated and evaporated automotive gasoline samples. , 2004, Forensic science international.

[22]  M. S. Fernandes,et al.  The effect of volatile residues in burnt household items on the detection of fire accelerants , 2002 .

[23]  M. Ferreiro-González,et al.  Gasoline analysis by headspace mass spectrometry and near infrared spectroscopy , 2015 .

[24]  Marta Ferreiro-González,et al.  Study of the Weathering Process of Gasoline by eNose , 2018, Sensors.

[25]  S. Coulson,et al.  The effect of compressed air foam on the detection of hydrocarbon fuels in fire debris samples. , 2000, Science & justice : journal of the Forensic Science Society.

[26]  J. Harynuk,et al.  Automated optimization and construction of chemometric models based on highly variable raw chromatographic data. , 2011, Analytica chimica acta.

[27]  M. H. Mach,et al.  Gas Chromatography-Mass Spectrometry of Simulated Arson Residue Using Gasoline as an Accelerant , 1977 .

[28]  Roman M. Balabin,et al.  Gasoline classification using near infrared (NIR) spectroscopy data: comparison of multivariate techniques. , 2010, Analytica chimica acta.

[29]  Mary R. Williams,et al.  Progress Toward the Determination of Correct Classification Rates in Fire Debris Analysis II: Utilizing Soft Independent Modeling of Class Analogy (SIMCA) , 2014, Journal of forensic sciences.

[30]  Mary R. Williams,et al.  Biodegradation of representative ignitable liquid components on soil , 2017 .

[31]  Kenneth G. Furton,et al.  Characterization of background and pyrolysis products that may interfere with the forensic analysis of fire debris , 2004 .

[32]  John D. DeHaan Ph.D. Fabc FSSDip,et al.  Kirk's Fire Investigation , 2011 .

[33]  M. Ferreiro-González,et al.  Validation of an HS-MS method for direct determination and classification of ignitable liquids , 2017 .

[34]  Thierry Talou,et al.  MOS–MOSFET gas sensors array measurements versus sensory and chemical characterisation of VOC’s emissions from car seat foams , 2003 .

[35]  M. Ferreiro-González,et al.  Application of an HS-MS for the detection of ignitable liquids from fire debris. , 2015, Talanta.