Search Results (7)

Producing defensible data for legal proceedings requires strict monitoring of sample integrity. In fire debris analysis, various approved packaging and storage solutions are designed to achieve this by preventing cross-contamination. This study examines the efficiency of current practices at preventing cross-contamination in the presence of a sample matrix (charred wood) via analysis by comprehensive multidimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-ToF MS). The transfer of ignitable liquid residue (ILR) was assessed by comparing percentages of the target ILR area relative to the total chromatogram area and applying chemometric tools developed to detect cross-contamination. All practices reduced cross-contamination in comparison to faulty packaging. Individual practices varied in their performance. Nylon-based packaging performed best, whereas commercial polyethylene-based packaging performed worst due to interfering compounds emitted from the material and sealing mechanism. Heat-sealing was the best sealing mechanism when applied correctly, followed by press-fit connections, and lastly, adhesive sealing. Refrigerated storage offered several advantages, with elevated impact for polyethylene-based packaging and adhesive sealing mechanisms. Triple-layer packaging practices did not show significant benefits over double-layers. The recommended packaging approach based on these findings is mixed-material packaging (metal quart can in a heat-sealed nylon bag), offering advanced prevention of cross-contamination and practical advantages with continued refrigeration during transport. Full article

Ignitable liquid residue (ILR) samples play an important role in fire investigations. Similar to other types of forensic evidence, maintaining sample integrity depends on the prevention of cross-contamination during both storage and transport. This study examines cross-contamination in ILR samples on various sample matrices (gravel, soil, wood). After inducing leaks in a controlled environment, sample analysis by GC×GC-ToF MS allowed for sensitive detection and in-depth characterization of cross-contamination processes. The potential for false positive identification of ILR is notably present due to cross-contamination. Compound transmission for a mid-range ILR (gasoline), for instance, was detectable after a 1 h exposure, with a complete profile transfer occurring after 8 h regardless of the matrix type. Visual comparisons and uptake rate calculations further confirmed matrix interaction effects taking place in the form of inherent native compound interference and adsorbate–adsorbate interaction during transmission and extraction processes for soil and wood matrices. Chemometric analysis highlighted the advantage of employing statistical analysis when investigating samples under matrix interactions by identifying several statistically significant compounds for reliably differentiating cross-contamination from background and simulated positive samples in different volatility ranges and compound classes. Untargeted analysis tentatively identified three additional compounds of interest within compound classes not currently investigated in routine analysis. The resulting classification between background, contaminated, and simulated positive samples showed no potential for false positive ILR identification and improved false negative errors, as evidenced by classification confidences progressing from 88% for targeted and 93% for untargeted to 95% for a diagnostic ratio analysis of three ratios deployed in tandem. Full article

Convolutional neural networks (CNNs) are inspired by the visual cortex of the brain. In this work, CNNs, are applied to classify ground truth samples as positive or negative for ignitable liquid residue (ILR+ and ILR−, respectively). Known ground truth samples included laboratory-generated fire debris samples, neat ignitable liquids (ILs), single-substrate (SUB) burned samples and computationally generated (in silico) training samples. The images were generated from the total ion spectra for both training and test datasets by applying a wavelet transformation. The training set consisted of 50,000 in silico-generated fire debris samples. The probabilities generated from the CNN are used to calculate the likelihood ratios. These likelihood ratios were calibrated using logistic regression and the empirical cross-entropy (ECE) plots were used to investigate the calibration of the probabilities of the presence of ILRs (i.e., probability of belonging to class ILR+). The performance of the model was evaluated by the area under the receiver operating characteristic plots (ROC AUC). The ROC AUC for the laboratory-generated fire debris samples and the combined IL and SUB samples was 0.87 and 0.99, respectively. The CNNs trained on in silico data did significantly better predicting the classification of the pure IL (ILR+) and SUB (ILR−) samples. Nonetheless, the classification performance for laboratory-generated samples was sufficient to aid forensic analysts in the classification of casework samples. Full article

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. Full article

In cases of suspected arson, a body may be intentionally burnt to cause loss of life, dispose of remains, or conceal identification. A primary focus of a fire investigation, particularly involving human remains, is to establish the cause of the fire; this often includes the forensic analysis of fire debris for the detection of ignitable liquid residues (ILRs). Commercial containers for the collection of fire debris evidence include metal cans, glass jars, and polymer/nylon bags of limited size. This presents a complication in cases where the fire debris consists of an intact, or partially intact, human cadaver. This study proposed the use of a body bag as an alternative sampling container. A method was developed and tested for the collection and analysis of ILRs from burnt porcine remains contained within a body bag using dynamic headspace sampling (using an Easy-VOC™ hand-held manually operated grab-sampler and stainless steel sorbent tubes containing Tenax TA) followed by thermal desorption comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (TD-GC×GC-TOFMS). The results demonstrated that a body bag containing remains burnt with gasoline tested positive for the presence of gasoline, while blank body bag controls and a body bag containing remains burnt without gasoline tested negative. The proposed method permits the collection of headspace samples from burnt remains before the remains are removed from the crime scene, limiting the potential for contamination and the loss of volatiles during transit and storage. Full article

In arson attacks the detection of ignitable liquid residues (ILRs) at fire scenes provides key evidence since ignitable liquids, such as gasoline, are commonly used to initiate the fire. In most forensic laboratories gas chromatography-mass spectrometry is employed for the analysis of ILRs. When a fire occurs, suppression agents are used to extinguish the fire and, before the scene is investigated, the samples at the scene are subjected to a variety of processes such as weathering, which can significantly modify the chemical composition and thus lead to erroneous conclusions. In order to avoid this possibility, the application of chemometric tools that help the analyst to extract useful information from data is very advantageous. The study described here concerned the application of a headspace-mass spectrometry electronic nose (HS-MS eNose) combined with chemometric tools to determine the presence/absence of gasoline in weathered fire debris samples. The effect of applying two suppression agents (Cafoam Aquafoam AF-6 and Pyro-chem PK-80 Powder) and delays in the sampling time (from 0 to 48 h) were studied. It was found that, although the suppression systems affect the mass spectra, the HS-MS eNose in combination with suitable pattern recognition chemometric tools, such as linear discriminant analysis, is able to identify the presence of gasoline in any of the studied situations (100% correct classification). Full article

Arsonists usually use an accelerant in order to start or accelerate a fire. The most widely used analytical method to determine the presence of such accelerants consists of a pre-concentration step of the ignitable liquid residues followed by chromatographic analysis. A rapid analytical method based on headspace-mass spectrometry electronic nose (E-Nose) has been developed for the analysis of Ignitable Liquid Residues (ILRs). The working conditions for the E-Nose analytical procedure were optimized by studying different fire debris samples. The optimized experimental variables were related to headspace generation, specifically, incubation temperature and incubation time. The optimal conditions were 115 °C and 10 min for these two parameters. Chemometric tools such as hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) were applied to the MS data (45–200 m/z) to establish the most suitable spectroscopic signals for the discrimination of several ignitable liquids. The optimized method was applied to a set of fire debris samples. In order to simulate post-burn samples several ignitable liquids (gasoline, diesel, citronella, kerosene, paraffin) were used to ignite different substrates (wood, cotton, cork, paper and paperboard). A full discrimination was obtained on using discriminant analysis. This method reported here can be considered as a green technique for fire debris analyses. Full article