Ignitable Liquid Residues Research Articles

Recovery and detection of ignitable liquid residues from the substrates by solid phase microextraction – direct analysis in real time mass spectrometry

In this study, direct analysis in real time mass spectrometry (DART-MS) was coupled to the solid phase microextraction (SPME) to extract and analyze the ignitable liquid residues (ILR) present in the sample matrices. The SPME extraction parameters, such as extraction temperature and extraction time, were optimized using a two-factor central composite design. The SPME-DART-MS setup was utilized to analyze the substrates and fire debris matrices spiked with gasoline. The results indicate that the less volatile marker compounds from gasoline were recovered from the substrates and fire debris, and their profiles matched well with the gasoline liquid samples analyzed directly by DART-MS. As expected, the effective extraction of marker compounds in gasoline required a relatively high temperature, i.e., 150 ℃. In the presence of a matrix, a higher extraction temperature and longer extraction time could benefit the extraction efficiency. The desorption of ILR on SPME fiber was performed by inserting the fiber into the DART-MS helium gas stream at 300 ℃ for 1 min with no carry-over residues being observed between successive samples. The chemical information attained with this method is typically not observed in the current GC/MS-based practice. The SPME-DART-MS was also extended to reanalyze less volatile components of ILR on substrates after the ASTM E1412 activated charcoal method, which indicates its possible application subsequent to the traditional GC/MS ILR analysis. The SPME-DART-MS has shown promise in ILR detection as an important complementary tool.

Detection of gasoline residues on household materials up to 60 days: Comparison of two extinguishing methods

Detection of ignitable liquid residues in a fire scene is essential for determining the origin. Although studies are focused on the detection of residues of accelerants depending on time or matrices, the time-dependent effect of the water extinguishing method in a fire has not yet been investigated. Experimental studies are needed to determine how long ignitable liquid residues can be detected in water-extinguished evidence compared to the smothering method. In this study, the effects of both extinguishing methods on gasoline residues were investigated after burning of carpet, sofa fabric, tablecloth, and towel by Solid Phase Micro Extraction- Gas Chromatography/Mass Spectrometry (SPME-GC/MS) technique. Four mandatory and 14 additional compounds were considered to prove the gasoline residue after the monitoring of possible interferences. Results showed that gasoline residues on the burned carpet and sofa fabric samples were successfully detected in both extinguishing methods up to 60 and 30 days after fire exposure, respectively due to multi-layered structures of related substrates. Additionally, the prolonged detection time of the water-extinguishing method made it particularly beneficial for single-layered products like tablecloths, where gasoline residues were found after an hour in this substrate. This is the first study investigating the effects of the extinguishing methods depending on time for textile products, which are the most used materials in houses. In addition, the fact that acrylamide-containing sofa fabric was investigated for the first time and that gasoline residues in carpet samples can be detected up to 60 days makes this study stand out.

Hydrocarbon retention on activated carbon: Preservation of fire debris evidence

Fire debris is collected and analyzed to determine whether volatile hydrocarbons of ignitable liquids (IL) are present. Typically, the hydrocarbons from fire debris are separated from the debris by adsorption onto activated carbon. Laboratory generated fire debris were sampled by adsorption onto activated charcoal strips (ACS) where half of the ACS was analyzed by gas chromatography – mass spectrometry (GC–MS) and the other half was preserved for 14 years [1,2]. Reference ignitable liquids are important in the detection and identification of ignitable liquid residues (ILR) from fire debris [3]. Reference IL contain 400 µl of IL adsorbed onto 0.5 g of granular activated carbon (GAC) and were preserved for 21 years. Comparisons of the chromatographic profiles at Day 0 and Year X (X = 14 or 21) were performed by Spearman rank correlation of selected peak’s intensities and visual inspection to determine how well these samples were preserved. The fire debris samples on ACS had an average Spearman rank correlation coefficient of 0.90 with a standard deviation of 0.11. Thirty-six percent of them demonstrated no change in the chromatographic profile and 64 % of them demonstrated minimal desorption in the chromatographic profile. The GAC samples had an average Spearman rank correlation coefficient of 0.95 with a standard deviation of 0.12. Eighty-two percent of them demonstrated no change in the chromatographic profile. This study reveals that hydrocarbons of ignitable liquids on activated carbon following established preservation procedures remain useful for forensic purposes after long-term storage.

Cross-Contamination of Ignitable Liquid Residues on Wildfire Debris—Effects of Packaging and Storage on Detection and Characterization

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.

Evaluation of efficient field detection methods for fire debris containing ignitable liquid residue

Evaluation of efficient field detection methods for fire debris containing ignitable liquid residue

Comparison of decision tree and naïve Bayes algorithms in detecting trace residue of gasoline based on gas chromatography-mass spectrometry data.

Fire debris analysis aims to detect and identify any ignitable liquid residues in burnt residues collected at a fire scene. Typically, the burnt residues are analysed using gas chromatography-mass spectrometry (GC-MS) and are manually interpreted. The interpretation process can be laborious due to the complexity and high dimensionality of the GC-MS data. Therefore, this study aims to compare the potential of classification and regression tree (CART) and naïve Bayes (NB) algorithms in analysing the pixel-level GC-MS data of fire debris. The data comprise 14 positive (i.e. fire debris with traces of gasoline) and 24 negative (i.e. fire debris without traces of gasoline) samples. The differences between the positive and negative samples were first inspected based on the mean chromatograms and scores plots of the principal component analysis technique. Then, CART and NB algorithms were independently applied to the GC-MS data. Stratified random resampling was applied to prepare three sets of 200 pairs of training and testing samples (i.e. split ratio of 7:3, 8:2, and 9:1) for estimating the prediction accuracies. Although both the positive and negative samples were hardly differentiated based on the mean chromatograms and scores plots of principal component analysis, the respective NB and CART predictive models produced satisfactory performances with the normalized GC-MS data, i.e. majority achieved prediction accuracy >70%. NB consistently outperformed CART based on the prediction accuracies of testing samples and the corresponding risk of overfitting except when evaluated using only 10% of samples. The accuracy of CART was found to be inversely proportional to the number of testing samples; meanwhile, NB demonstrated rather consistent performances across the three split ratios. In conclusion, NB seems to be much better than CART based on the robustness against the number of testing samples and the consistent lower risk of overfitting.

Cross-Contamination of Ignitable Liquid Residues on Wildfire Debris—Detection and Characterization in Matrices Commonly Encountered at Wildfire Scenes

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.

Assessing the impact of habitat and captivity status on volatilome profiles of the illegally traded shingleback, Tiliqua rugosa

Volatilomics is the study of the total biogenic volatile organic compounds (BVOCs) produced by an organism. This field has been used to assess organism and ecosystem health, as well as determine BVOC biomarkers for forensic purposes, including the detection of human remains, ignitable liquid residues and illicit drugs. For volatilomics to be applied in wildlife-victim casework (e.g. the illegal wildlife trade) a large reference database must be collected across each targeted species range. Adequate sample sizes must be collected from different habitats spanning across the species range of the targeted species to evaluate volatilome variability associated with different environmental and dietary characteristics. This will allow for assessment of chemical diversity and the determination of BVOC biomarkers that are relevant to wildlife forensic cases (e.g. detection, species identification, geographic origin assessment). This study collected the first live animal volatilome database, using the highly trafficked and widely distributed Australian shingleback lizard (Tiliqua rugosa). Optimised thermal desorption and analysis methods were used to examine 127 wild shingleback volatilome samples collected from sites across New South Wales, South Australia and Western Australia and 28 volatilome samples from captive shinglebacks. The results demonstrated that volatilome profiles and chemical diversity differed across each sampling region, potentially related to habitat and diet changes. At least 7 volatilome samples were required to capture chemical diversity in a sampling region. Forty-four tentatively identified BVOCs were shared across all sampling regions and captive animals which may aid in detection purposes. Bioregion-specific BVOCs were also identified, which will also aid in geographic assignment of confiscated individuals. This work demonstrates the importance of sample sizes in capturing chemical diversity within Bioregions prior to downstream volatilome analysis for the establishment of wildlife forensic databases and biomarker selection.

Analyst and machine learning opinions in fire debris analysis

The principles of subjective logic are applied to the competing propositions that ignitable liquid residue (ILR) is present, or is not present, in a fire debris sample. Analysts’ estimates of the strength of evidence coupled with their perceived levels of uncertainty combine to define a “fuzzy category” that is mapped to an opinion triangle. The opinion is expressed as a tuple consisting of the belief mass, disbelief mass, uncertainty and base rate. A workflow is introduced to guide the analyst through the fuzzy category formulation. Opinion tuples are also generated from a set of machine learning (ML) models trained on an ensemble of data sets. A set of 20 single-blind fire debris samples were analyzed by each of the authors, and by an ensemble of optimized support vector machine models. The opinions of each analyst and the ML ensemble were compared and combined to obtain an opinion representing a consensus of each analyst and the ML. The opinions of the analysts and ML were projected onto the zero-uncertainty axis and the projected opinion probabilities were used as scores to construct an receiver operating characteristic (ROC) curve. The area under the ROC curves for each analyst were greater than or equal to 0.90 and the area under the ML ROC curve was 0.96. The methodology is widely applicable to forensic problems that can be represented as a pair of mutually exclusive and exhaustive hypotheses.

Concentration of ignitable liquid residue from simulated fire debris by dynamic vapor microextraction: Sensitivity to instrument settings and debris characteristics

Dynamic vapor microextraction (DVME) is a potential method for the extraction and concentration of ignitable liquid (IL) residue in fire debris. This low flow rate, purge-and-trap headspace concentration method collects IL vapors onto a chilled adsorbent capillary and recovers them by elution with acetone. As an emerging method for fire debris analysis, the sensitivity of DVME performance to instrument settings has yet to be established and, additionally, the effect of variability inherent in authentic fire debris (e.g., water content) has not yet been explored. In this work, we quantitatively evaluate the effect of 11 factors via a sensitivity analysis with simulated fire debris. The factors studied included six controllable instrument settings and five reflecting debris characteristics. We quantified performance by covariance mapping between gas chromatography – mass spectrometry (GC–MS) retention time – ion abundance matrices for the recovered eluates and corresponding reference samples. Six factors were found to be significant. IL volume, IL weathering, and debris quantity significantly affected the recovered eluates, whereas water content did not. As related to recovering IL residue from simulated fire debris, recommended instrument settings include a higher oven temperature, longer equilibration time, larger volume of extracted headspace (collection volume), and a lower inlet flow rate. Together with the covariance mapping metric, the fractional factorial design successfully addressed questions about the effect of instrument factors, debris factors, and their interactions with an efficient number of experiments.

Sampling and recovery of ignitable liquid residues (ILRs) from fire debris using capillary microextraction of volatiles (CMV) for on‐site analysis

A new, fast, and ultra-sensitive headspace sampling method using the Capillary Microextraction of Volatiles (CMV) device is demonstrated for the analysis of ignitable liquid residues (ILRs) in fire debris. This headspace sampling method involves the use of a heated can (60°C) to aid in the recovery of volatile organic compounds (VOCs) from medium and heavy petroleum distillates. Our group has previously reported the utility of CMV to extract gasoline at ambient temperature in less than 5min in the field. This work evaluates the recovery and analysis of low mass loadings (tens of ng) of VOCs from charcoal lighter fluid, kerosene, and diesel fuel. Nonane, decane, undecane, tridecane, tetradecane, and pentadecane were selected for evaluation of recovery to represent these ILR classes. The face-down heated can headspace sampling technique was compared to the previously reported, non-heated, paper cup headspace sampling technique. Mass recovery improvements of 50%-200% for five of the six target compounds in diesel fuel were achieved compared to the non-heated sampling method. The average relative standard deviation (reported as % RSD) between the replicate trials decreased from an average of 28% to 6% when using the heated can method. Ignitable liquids were spiked onto burned debris in a live burn exercise and sampled using the heated can and paper cup headspace sampling techniques. The heated sampling technique reported here, for the first time, demonstrates an effective extraction method that when coupled to a portable GC-MS instrument allows for a sampling and analysis protocol in the field in less than 30 min.

Convolutional Neural Network Applications in Fire Debris Classification

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.

Sensitive and Representative Extraction of Petroleum-Based Ignitable Liquids From Fire Debris For Confirmatory Analysis of Canine-Selected Exhibits

Fire debris analysis is focused on the recovery and identification of ignitable liquids to provide context for fire investigation. Investigators use a variety of methods to select suspicious debris for analysis, with ignitable liquid detection canines being one of the most popular. When properly trained and certified, ignitable liquid detection canines offer continuous sampling with high sensitivity and the ability to discriminate between irrelevant and suspicious odours to rapidly locate debris which may contain ignitable liquid residues. However, canine indications are presumptive as they cannot be sufficiently scrutinised by the legal process without confirmatory laboratory analysis. Standard debris analysis methods detect very small amounts of ignitable liquid residue (∼1-0.1 μL) without maximising sensitivity which minimises the risk from false positives and from detection of background petroleum which is ubiquitous in our environment. For canine-selected debris, the goal of the laboratory analysis should be to provide data to confirm or refute the validity of the canine indication. For such confirmatory analysis to be useful, analytical sensitivity should approximate the sensitivity of the canine. The sensitivity of fire debris analysis is most influenced by the selection of the extraction device and tuning of extraction conditions. Non-destructive extractions are preferred for forensic analyses, and solid phase microextraction (SPME) offers an excellent option. However, the original SPME fibres are fragile and tend to skew the chromatographic profile which can lead to high costs and a risk of ignitable liquid misclassification. Herein, we present an optimised SPME extraction method suited to confirmatory analysis of canine-selected exhibits. The method is non-destructive and non-exhaustive, is easily applied to cans of debris, and yields chromatographic profiles equivalent to those obtained by the gold-standard passive headspace sampling (PHS) methods based on activated carbon. Fibre selection, debris temperature, fibre temperature, and extraction time were optimised to yield chromatographic profiles with maximum comparability to reference samples collected as neat liquids or standard PHS extracts. The optimised method is applied to samples recovered from another study which estimated the threshold of the canine’s sensitivity, with the laboratory result compared to the canine result for each sample.

Decision theory and linear sequential unmasking in forensic fire debris analysis: A proposed workflow

The ASTM E1618-19 standard method for fire debris analysis does not endorse the use of statistical analysis to establish error rates or the use of probabilistic statements about the strength of the evidence. The method requires the analyst to report in categorical terms with the possibility of introducing disclaimers. This report introduces a new workflow that extends the data analysis and interpretation methods of ASTM E1618-19. In the new workflow, analysts are asked to assign evidentiary values to fire debris samples. The assigned values are combined with decision theory to determine an optimal decision threshold for ascribing a sample as positive or negative for ignitable liquid residue in categorical terms. The new workflow incorporates linear sequential unmasking with the data analysis methods of ASTM E1618-19, machine learning likelihood ratios and the use of ignitable liquid and substrate databases. The workflow is applied to 20 known ground truth samples by each of three analysts to generate validated personal receiver operating characteristic (ROC) curves and optimal decision thresholds. The ROC curve provides a direct link between perceived evidentiary value and decision thresholds based on a defined ratio of costs for false positive and false negative decisions. The new workflow is applied to casework-relevant large-scale burn data for which the ground truth is unknown.

Effect of kerosene combustion atmosphere on the mild steel oxide layer

In arson cases, accelerants were usually used by criminals to achieve the purpose of rapid arson. Therefore, fire investigators aim to determine whether accelerants was used in the fire scene. Metallic material has to react with corrosive gas around it at high temperature and the oxidation products may store the information of reactants. Accelerants present in fire scenes impart some oxidative characteristics on metallic materials. The aim of this work is to figure out the possibility to identify the presence of accelerant in a fire according to the oxidation patterns of metallic material. This paper researched the oxidation behavior of mild steel at high temperature in a simulated flame environment. The surface morphological and cross-sectional microstructural features of the samples were characterized by X-ray diffractions, X-ray photoelectron spectroscopy and scanning electron microscopy with energy-dispersive spectroscopy analysis after oxidation. The carbon in the combustion atmosphere had a carburizing effect on the metal oxide layer. It was mostly C–C, C–O and C=O of organic matter could be used as in fire investigation. Various oxidizing atmosphere composite systems promote the formation of metal oxide layers. And bidirectional oxidation mode in the oxide layer further accelerates the oxidation rate. The (wustite) FeO phase was not found in the oxide layer because of the strong oxidation of the combustion atmosphere. These results offer complementary information in fire characteristics, which combining the characterization of surface scale with traditional chemical analysis of recovering ignitable liquid residues from fire debris are expected to offer crucial information for determining the presence of combustion accelerants at a fire scene.

Recognition of gasoline in fire debris using machine learning: Part II, application of a neural network

The recognition of ignitable liquid (IL) residues in fire debris is a resource intensive but key part of an arson investigation. Due to the highly diverse and heavily loaded chemical matrix of fire debris samples, combined with the broad chemical composition of IL, the interpretation of the laboratory analysis results is a very challenging task for the forensic examiner. Fire debris samples are commonly analyzed using gas chromatography coupled to mass spectrometry (GC-MS). This method delivers both the total ion chromatogram (TIC) with the individually separated compounds and the underlying mass spectrum of each of the separated compounds. In this study, a completely new approach for the recognition of gasoline in fire debris samples is presented. First, the GC-MS data, including retention time, signal intensity, and mass spectrum is converted into a bitmap image. Five different data-to-image conversion approaches are tested, and their advantages and limitations are discussed. Subsequently, a convolutional neural network (CNN) is utilized to allocate the generated images to the classes “with gasoline” or “without gasoline”. The applied approaches to generate a digital image and the pattern recognition of the CNN perform very well in the classification of unknown test samples. Depending on the data-to-image generation approach used, the rate of correct sample classification in the test dataset is between 95% and 98%. The machine learning approach in this study, as well as the complementary method presented in an accompanying article, are not only useful for the recognition of gasoline in fire debris but are equally applicable to any additional areas in which the interpretation of complex chromatographic and mass spectrometric is required.

Evaluation of capillary microextraction of volatiles (CMV) coupled to a person-portable gas chromatograph mass spectrometer (GC–MS) for the analysis of gasoline residues

A novel dynamic headspace extraction device, the capillary microextraction of volatiles (CMV) was coupled to a person-portable GC–MS for the analysis of ignitable liquid residues (ILR) sampled from simulated fire debris. A benchtop GC–MS was used as a benchmark for the performance of the portable GC–MS. The use of a paper cup in conjunction with the CMV for in-field sampling of the VOCs associated with ILRs is presented for the first time. A five-minute sampling/extraction protocol was sufficient to recover six (6) analytes: toluene, ethylbenzene, m-xylene, o-xylene, 4-ethyltoluene, and 1,2,4-trimethylbenzene from a 0.01 µL spike of gasoline, with typical mass recoveries of 4–24 ng. Extractions from water-logged debris resulted in reliable detection of the same six compounds but up to 62% less was retained relative to dry debris. Recoveries for detected analytes ranged between 1 and 5% at several solution spike volumes suggesting proportional retention by the CMV. The CMV also demonstrated greater extraction capabilities than the portable GC–MS air sampling wand. Out of a 20-component mixture, 17 compounds were detected compared to the wand’s 13, with the majority of these at higher overall intensities for CMV. An overall 21-minute analytical method was developed using the CMV/Cup protocol capable of detecting several ILR-associated compounds at up to 10x greater sensitivity than traditional extraction techniques such as activated charcoal strips and SPME fibers.

Recognition of gasoline in fire debris using machine learning: Part I, application of random forest, gradient boosting, support vector machine, and naïve bayes

The detection and identification of ignitable liquid (IL) residues in fire debris are two very challenging tasks in a fire investigation. To this day, the recognition of IL in fire debris includes the chemical analysis of the fire debris composition, followed by the examination and interpretation of the analysis result by a trained forensic examiner. Throughout the last decade, chemometrics and artificial intelligence have become increasingly important. In the present study, machine learning algorithms capable of recognizing gasoline residues in fire debris based on GC-MS data have been developed. Four methods, including random forest, gradient boosting, support vector machine, and naïve bayes are applied and used to classify fire debris samples into the two categories “with gasoline” or “without gasoline”. A fifth method (logistic regression) did not converge due to well separated classes. A database comprising 360 measurements, including fire debris samples of real cases as well as fire debris samples spiked with known amounts of weathered gasoline (up to 99.6%), was available to train the machine learning algorithms (using 85% of the data) and to subsequently test the performance of the methods when classifying unknown samples (using 15% of the data). In general, the methods perform very well, as three of it succeeded to classify all test samples correctly without any false positive or false negative allocations. One (naïve bayes) was not trained enough to classify other (non-gasoline) IL correctly as “no gasoline”. Furthermore, the random forest method reveals which chemical compounds are most relevant for the algorithm to classify the samples. In general, the presented approach is highly promising and could easily be extended or adapted to other types of IL. Similar to the neural network presented in the accompanying paper, such methods have the potential to serve as a fast screening technique for fire debris samples, thus supporting the forensic examiner by providing an additional independent opinion. Nonetheless, the definite identification of IL residues in fire debris always has to be accomplished by a forensic examiner.

Activated Charcoal Pellets as an Innovative Method for Forensic Analysis of Ignitable Liquid Residues from Fire Debris by GC-MS

In Forensic Chemistry, evidence collected at a crime scene is of paramount importance for any case to be properly elucidated. Ignitable liquid residues are important chemical evidence in investigations into cases of fire because these substances can be correlated to arson. Here, we describe an innovative technique for sampling and extracting gasoline and diesel from fire debris by using activated charcoal pellets (ACP). ACP can be an alternative to activated charcoal strips and can be easily produced on the laboratory scale. The ACP approach allowed all the target compounds selected for gasoline and diesel fuels to be extracted. Among the six tested extraction conditions, optimal extraction occurred at 100 °C, after 240 min. These preliminary results showed the potential of ACP for detecting gasoline and diesel in fire debris. However, the ACP approach still requires analytical validation, so that its applicability in an authentic forensic setting can be explored.

Interference from combustion residues of polystyrene-butadiene involving ‘alkylbenzenes’ in molecules on gasoline identification

ABSTRACT The interference of background at fire scene on identification of ignitable liquid residues (ILRs) is an ongoing concern in fire investigation. The current study aims to determine potential interference on gasoline identification from the combustion/pyrolysis products of polystyrene-butadiene rubber (SBr) involving ‘alkylbenzenes’ in molecules. Gas chromatography–mass spectrometry (GC/MS) analysis results of extracts from combustion residues of SBrs, SBrs loaded with gasoline and gasoline were compared. Commonly encountered compounds interfering gasoline identification, including the alkylbenzenes, naphthalenes, as well as indanes, were obtained in all the combustion residues of the SBrs by analysing the extracted ion chromatogram (EICs) of the combustion/pyrolysis products. Furthermore, for gasoline loaded onto SBr substrate, the SBr causes significant distortion to gasoline combustion residues by changing the comparative contents of the target compounds for gasoline identification. Overreliance on the target compounds for gasoline identification could lead to misidentifications in the presence of SBrs combustion residues, while the typical ratios of the target compounds in gasoline combustion residues would also be affected when encountered with SBrs, especially for the cases of smaller loading of gasoline. A detailed examination of the C3-alkylbenzenes and C4-alkylbenzenes profile should be part of any fire debris analysis.