Characterization of Biodegraded Ignitable Liquids by Headspace-Ion Mobility Spectrometry

Assessment of Volatile Compound Transference through Firefighter Turnout Gear

There is high concern about the exposure of firefighters to toxic products or carcinogens resulting from combustion during fire interventions. Firefighter turnout gear is designed to protect against immediate fire hazards but not against chemical agents. Additionally, the decontamination of firefighter personal protective equipment remains unresolved. This study evaluated the feasibility of a screening method based on headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) in combination with chemometrics (cluster analysis, principal component analysis, and linear discriminant analysis) for the assessment of the transference of volatile compounds through turnout gear. To achieve this, firefighter turnout gears exposed to two different fire scenes (with different combustion materials) were directly analyzed. We obtained a spectral fingerprint for turnout gears that were both exposed and non-exposed to fire scenes. The results showed that (i): the contamination of the turnout gears is different depending on the type of fire loading; and (ii) it is possible to determine if the turnout gear is free of volatile compounds. Based on the latest results, we concluded that HS-GC-IMS can be applied as a screening technique to assess the quality of turnout gear prior to a new fire intervention.

A Novel Method Based on Headspace-Ion Mobility Spectrometry for the Detection and Discrimination of Different Petroleum Derived Products in Seawater

The objective of the present study is to develop an optimized method where headspace-ion mobility spectrometry is applied for the detection and discrimination between four petroleum-derived products (PDPs) in water. A Box–Behnken design with a response surface methodology was used, and five variables (incubation temperature, incubation time, agitation, sample volume, and injection volume) with influences on the ion mobility spectrometry (IMS) response were optimized. An IMS detector was used as a multiple sensor device, in which, each drift time acts as a specific sensor. In this way, the total intensity at each drift time is equivalent to multiple sensor signals. According to our results, 2.5 mL of sample incubated for 5 min at 31 °C, agitated at 750 rpm, and with an injection volume of 0.91 mL were the optimal conditions for successful detection and discrimination of the PDPs. The developed method has exhibited good intermediate precision and repeatability with a coefficient of variation lower than 5%, (RSD (Relative Standard Deviation): 2.35% and 3.09%, respectively). Subsequently, the method was applied in the context of the detection and discrimination of petroleum-derived products added to water samples at low concentration levels (2 µL·L⁻¹). Finally, the new method was applied to determine the presence of petroleum-derived products in seawater samples.

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

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.