scholarly journals Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 801
Author(s):  
Barbara Falatová ◽  
Marta Ferreiro-González ◽  
José Luis P. Calle ◽  
José Ángel Álvarez ◽  
Miguel Palma
Keyword(s):  
Accurate Determination ◽  
Hierarchical Cluster ◽  
Mass Spectrometry Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Linear Discriminant ◽  
Synthetic Fibers ◽  
Ignitable Liquids ◽  
Fire Debris ◽  
American Society ◽  
Ignitable Liquid

Interpretation of data from fire debris is considered as one of the most challenging steps in fire investigation. Forensic analysts are tasked to identify the presence or absence of ignitable liquid residues (ILRs) which may indicate whether a fire was started deliberately. So far, data analysis is subjected to human interpretation following the American Society for Testing and Materials’ guidelines (ASTM E1618) based on gas chromatography–mass spectrometry data. However, different factors such as interfering pyrolysis compounds may hinder the interpretation of data. Some substrates release compounds that are in the range of common ignitable liquids, which interferes with accurate determination of ILRs. The aim of the current research is to investigate whether headspace–mass spectroscopy electronic nose (HS-MS eNose) combined with pattern recognition can be used to classify different ILRs from fire debris samples that contain a complex matrix (petroleum-based substrates or synthetic fibers carpet) that can strongly interfere with their identification. Six different substrates—four petroleum-derived substrates (vinyl, linoleum, polyester, and polyamide carpet), as well as two different materials for comparison purposes (cotton and cork) were used to investigate background interferences. Gasoline, diesel, ethanol, and charcoal starter with kerosene were used as ignitable liquids. In addition, fire debris samples were taken after different elapsed times. A total of 360 fire debris samples were analyzed. The obtained total ion mass spectrum was combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) as well as supervised linear discriminant analysis (LDA). The results from HCA show a strong tendency to group the samples according to the ILs and substrate used, and LDA allowed for a full identification and discrimination of every ILR regardless of the substrate.

scholarly journals Application of Self-Organizing Maps to the Analysis of Ignitable Liquid and Substrate Pyrolysis Samples

Separations ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 52 ◽  
Author(s):  
Nicholas Thurn ◽  
Mary Williams ◽  
Michael Sigman
Keyword(s):  
Standard Test ◽  
Test Method ◽  
Gas Chromatography Mass Spectrometry ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Ignitable Liquids ◽  
Fire Debris ◽  
Ignitable Liquid ◽  
Self Organizing

Classification of un-weathered ignitable liquids is a problem that is currently addressed by visual pattern recognition under the guidelines of Standard Test Method for Ignitable Liquid Residues in Extracts from Fire Debris Samples by Gas Chromatography-Mass Spectrometry, ASTM E1618-14. This standard method does not separately address the identification of substrate pyrolysis patterns. This report details the use of a Kohonen self-organizing map coupled with extracted ion spectra to organize ignitable liquids and substrate pyrolysis samples on a two-dimensional map with groupings that correspond to the ASTM-classifications and separate the substrate pyrolysis samples from the ignitable liquids. The component planes give important information regarding the ions from the extracted ion spectra that contribute to the different classes. Some additional insight is gained into grouping of substrate pyrolysis samples based on the nature of the unburned material as a wood or non-wood material. Further subclassification was not apparent from the self-organizing maps (SOM) results.

scholarly journals Application of Headspace Gas Chromatography-Ion Mobility Spectrometry for the Determination of Ignitable Liquids from Fire Debris

Separations ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 41 ◽  
Author(s):  
María Aliaño-González ◽  
Marta Ferreiro-González ◽  
Gerardo Barbero ◽  
Miguel Palma ◽  
Carmelo Barroso
Keyword(s):  
Gas Chromatography ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Headspace Gas Chromatography ◽  
Headspace Gas ◽  
Ignitable Liquids ◽  
Fire Debris ◽  
Ignitable Liquid ◽  
In Fire ◽  
Chemometric Tools

A fast and correct identification of ignitable liquid residues in fire debris investigation is of high importance in forensic research. Advanced fast analytical methods combined with chemometric tools are usually applied for these purposes. In the present study, the Headspace Gas Chromatography-Ion Mobility Spectrometry (HS-GC-IMS) combined with chemometrics is proposed as a promising technique for the identification of ignitable liquid residues in fire debris samples. Fire debris samples were created in the laboratory, according to the Destructive Distillation Method for Burning that is provided by the Bureau of Forensic Fire and Explosives. Four different substrates (pine wood, cork, paper, and cotton sheet) and four ignitable liquids of dissimilar composition (gasoline, diesel, ethanol, and paraffin) were used to create the fire debris. The Total Ion Current (TIC) Chromatogram combined with different chemometric tools (hierarchical cluster analysis and linear discriminant analysis) allowed for a full discrimination between samples that were burned with and without ignitable liquids. Additionally, a good identification (95% correct discrimination) for the specific ignitable liquid residues in the samples was achieved. Based on these results, the chromatographic data from HS-GC-IMS have been demonstrated to be very useful for the identification and discrimination of ignitable liquids residues. The main advantages of this approach vs. traditional methodology are that no sample manipulation or solvent is required; it is also faster, cheaper, and easy to use for routine analyses.

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