ignitable liquids
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2021 ◽  
pp. 100340
Author(s):  
Briana A. Capistran ◽  
Victoria L. McGuffin ◽  
Ruth Waddell Smith

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 801
Author(s):  
Barbara Falatová ◽  
Marta Ferreiro-González ◽  
José Luis P. Calle ◽  
José Ángel Álvarez ◽  
Miguel Palma

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.


Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6005
Author(s):  
José Luis P. Calle ◽  
Marta Ferreiro-González ◽  
María José Aliaño-González ◽  
Gerardo F. Barbero ◽  
Miguel Palma

The detection of ignitable liquids (ILs) can be crucial when it comes to determining arson cases. Such identification of ILs is a challenging task that may be affected by a number of factors. Microbial degradation is considered one of three major processes that can alter the composition of IL residues. Since biodegradation is a time related phenomenon, it should be studied at different stages of development. This article presents a method based on ion mobility spectroscopy (IMS) which has been used as an electronic nose. In particular, ion mobility sum spectrum (IMSS) in combination with chemometric techniques (hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA)) has been applied for the characterization of different biodegraded ILs. This method intends to use IMSS to identify a range of ILs regardless of their degree of biodegradation. Three ILs (diesel, gasoline and kerosene) from three different commercial brands were evaluated after remaining in a soil substrate for several lengths of time (0, 2, 5, 13 and 38 days). The HCA results showed the samples’ trend to fall into categories characterized by ILs type and biodegradation time. The LDAs allowed a 99% successful classification of the samples according to the IL type. This is the first time that an HS-IMS technique has been used to detect ILs that have undergone biodegradation processes. The results show that IMS may be a promising alternative to the current standard method based on gas-chromatography for the analysis of biodegraded ILs. Furthermore, no pretreatment of the samples nor the use of a solvent is required.


2020 ◽  
Author(s):  
Reta Newman ◽  
Michael Gilbert ◽  
Kevin Lothridge
Keyword(s):  

2020 ◽  
Author(s):  
Reta Newman ◽  
Michael Gilbert ◽  
Kevin Lothridge
Keyword(s):  

2020 ◽  
Vol 312 ◽  
pp. 110312
Author(s):  
Jamie Baerncopf
Keyword(s):  

2020 ◽  
Vol 312 ◽  
pp. 110309
Author(s):  
Venita Totten ◽  
Jacob Willis
Keyword(s):  

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