Evaluation of an untargeted chemometric approach for the source inference of ignitable liquids in forensic science

Figueiredo, Miguel de; Cordella, Christophe; Bouveresse, Delphine Jouan-Rimbaud; Archer, Xavier; Bégué, Jean-Marc; Rutledge, Douglas N.

doi:10.1016/j.forsciint.2018.11.016

Cited by 19 publications

(3 citation statements)

References 89 publications

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“…de Figueiredo et al used an untargeted chemometric approach with data obtained from 190 unique gasoline samples to attempt to differentiate the liquids [ 57 ]. Headspace concentration using Tenax TA tubes followed by automated thermodesorption GC-MS was used to collect the data.…”

Section: Fire Debris Analysismentioning

confidence: 99%

Interpol review of fire debris analysis and fire investigation 2019–2022

Evans¹

2023

Forensic Science International: Synergy

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Section: Fire Debris Analysismentioning

confidence: 99%

Interpol review of fire debris analysis and fire investigation 2019–2022

Evans¹

2023

Forensic Science International: Synergy

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“…In recent work that is related to Bayesian LRs described below, a set of 570 unevaporated gasoline samples were examined for the purpose of distinguishing samples originating from the same and different sources (de Figueiredo, Cordella, et al, ). The data analysis involved calculating ratios between all peaks in the GC–MS TICs and performing feature selection to identify peaks giving consistent ratios.…”

Section: Chemometric Studiesmentioning

confidence: 99%

Chemometric applications in fire debris analysis

Sigman

Williams

2020

WIREs Forensic Science

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Chemometric applications related to the analysis of ignitable liquids (ILs) and fire debris samples have evolved over the last 30 years. Most older papers focused on the use of chemometrics for classification of ILs by manufacturer, grade or into classes defined by the American Society for Testing and Material (ASTM) standard test method ASTM E1618. More recently, chemometric studies in fire debris analysis have focused on estimating the strength of the evidence in the form of a likelihood ratio (LR). The progression of chemometric methods from simple classification tasks to the calculation of LRs and statements of evidentiary value has implications across all disciplines of forensic science. Most of the data analyzed by chemometric methods in forensic fire debris analysis come from gas chromatography–mass spectrometry, although several studies have examined data from head space‐mass spectrometry and other sensors. Retention time alignment is a challenge that must be addressed if chemometric methods are intended to have interlaboratory applicability. Several of the reports reviewed here rely on data representations that avoid retention time alignment issues. Attempts to apply chemometric methods to the analysis of ILs and fire debris samples has for the most part been an academic exercise and the results have not made their way into accredited forensic laboratories and established protocols. The chemometric methods are well‐founded in statistical principles and they can provide a means of strengthening forensic fire debris analysis in the future. This article is categorized under: Forensic Chemistry and Trace Evidence > Fire Debris Analysis Forensic Chemistry and Trace Evidence > Trace Evidence Forensic Chemistry and Trace Evidence > Presentation and Evaluation of Forensic Science Output

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“…One proposed method is a likelihood ratio approach 8 , implicitly using a simple evaporation model. Other studies have used explicit evaporation models based on Raoult's law 9,10 or by using gas chromatographic retention data [11][12][13][14] , and other experimental methods 15,16 . All these methods suffer from major drawbacks, e.g., only a limited number of components can be dealt with, the methods cannot be applied to highly weathered samples, large experimental data sets are required, and most importantly, the methods cannot predict the initial (unweathered) composition of a weathered sample.…”

mentioning

confidence: 99%

Artificial intelligence and thermodynamics help solving arson cases

Korver

Schouten

Moultos

et al. 2020

Sci Rep

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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.

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Evaluation of an untargeted chemometric approach for the source inference of ignitable liquids in forensic science

Cited by 19 publications

References 89 publications

Interpol review of fire debris analysis and fire investigation 2019–2022

Interpol review of fire debris analysis and fire investigation 2019–2022

Chemometric applications in fire debris analysis

Artificial intelligence and thermodynamics help solving arson cases

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