Artificial intelligence and thermodynamics help solving arson cases.

In arson cases, evidence such as DNA or fingerprints is often destroyed. One of the most important evidence modalities left is relating fire accelerants to a suspect. When gasoline is used as accelerant, the aim is to find a strong indication that a gasoline sample from a fire scene is related to a sample of a suspect. Gasoline samples from a fire scene are weathered, which prohibits a straightforward comparison. We combine machine learning, thermodynamic modeling, and quantum mechanics to predict the composition of unweathered gasoline samples starting from weathered ones. Our approach predicts the initial (unweathered) composition of the sixty main components in a weathered gasoline sample, with error bars of ca. 4% when weathered up to 80% w/w. This shows that machine learning is a valuable tool for predicting the initial composition of a weathered gasoline, and thereby relating samples to suspects.

Using Alkylate Components for Classifying Gasoline in Fire Debris Samples.

The characteristic that discriminates gasoline from other ignitable liquids is that it contains high-octane blending components. This study elaborates on the idea that the presence of gasoline in fire debris samples should be based on the detection of known high-octane blending components. The potential of the high-octane blending component alkylate as a characteristic feature for gasoline detection and identification in fire debris samples is explored. We have devised characteristic features for the detection of alkylate and verified the presence of alkylate in a large collection of gasoline samples from petrol stations in the Netherlands. Alkylate was detected in the vast majority of the samples. It is demonstrated that alkylate can be detected in fire debris samples that contain traces of gasoline by means of routine GC-MS methods. Detection of alkylate, alongside other gasoline blend components, results in a more solid foundation for gasoline detection and identification in fire debris samples.

Toxicological findings in three cases of suicidal asphyxiation with helium.

INTRODUCTION: Toxicological findings in deaths by asphyxiation due to a pure inert gas like helium are rare. We present three suicide cases of asphyxial death attributed to anoxia caused by inhalation of helium in a plastic bag positioned over the head. METHODS: In one case, lung tissue, brain tissue and heart blood were obtained during standard autopsy procedures. In two cases, samples were obtained differently: heart blood, femoral blood, brain tissue, lung tissue and/or air from the lungs were directly sealed into headspace vials during autopsy. Air from the lungs was collected using a syringe and transferred into an aluminum gas sampling bag which was heat sealed as soon as possible. Semi-quantitative gas analyses were performed using headspace gas chromatography-thermal conductivity detection (HS-GC/TCD) with a molsieve column capable of separating permanent gasses. Nitrogen was used as carrier gas. RESULTS: In the first case no helium was detected in lung tissue, brain tissue and heart blood. In the second case the presence of helium was detected in lung tissue (approximately 5% helium in gaseous phase) but not in femoral blood. In the third case the presence of helium was detected in air from the lungs (0.05%), lung tissue (0.4%), brain tissue (0.1%) and heart blood (0.04%). CONCLUSIONS: Helium is easily lost if sampling is not performed properly. The presented cases suggest that quick sample collection of various matrices during autopsy is suitable to detect gasses like helium in postmortem cases. Use of HS-GC/TCD enables to detect an inert gas like helium.