Detection and characterization of volatile organic compounds from burned human and animal remains in fire debris.

Debris collected from various test sites where mammalian remains (human and porcine) had been burned in a variety of full-scale fire scenarios was evaluated for the presence of volatile residues that could be characteristic of those remains. Levels of volatiles were measured using the method commonly used for fire debris analysis: gas chromatography-mass spectrometry. Homologous n-aldehydes (from n-pentanal to n-nonanal) proved to be a significant indicator of the presence of burned animal tissue as they were observed in nearly all of the samples. Such aldehydes are created by the combustion of animal fats. One aldehyde, n-hexanal, appeared more frequently than the other aldehydes, n-pentanal, n-heptanal, n-octanal, and n-nonanal. Ethanol was detected in two-thirds of the samples, while acetone appeared in about three-fourths of the samples, but both were detected at much lower concentrations than n-hexanal. These appear to have been combustion products of the substrates on which each body burned, rather than originating from the combustion of the body. There appeared to be no qualitative distinction between volatile products produced from burned porcine carcasses and those from human cadavers. Since a homologous series of C5-C9n-aldehydes is not produced as a dominant species by the pyrolysis or combustion of any normally encountered substrate (carpet, bedding, wood products or upholstery), their detection by normal fire debris methods appears to be a valid indicator of the presence of burned animal remains. These data will also provide guidance to fire debris analysts as to the nature of volatiles associated with the combustion of human bodies in real-world fires.

Substrate interferences in identifying flammable liquids in food, environmental and biological samples: case studies.

The analysis of samples for traces of ignitable liquids is most often connected with suspected arson cases. In such cases, samples taken from the point of origin of the fire are analyzed for the presence of ignitable liquids. However, sometimes, in cases not connected with arson, there is a need to detect and identify traces of ignitable liquids. Three examples of such cases are given in this paper. Aqueous samples (polluted water, juice and blood) were analyzed using a procedure routinely used in the analyses of fire debris. The procedure consists of passive adsorption of volatile organic compounds on Tenax, followed by thermal desorption and chromatographic analysis. Results showed that analysis of such untypical samples may be connected with unusual matrix effects, not frequently encountered in fire debris samples.

Assessment of artificial intelligence to detect gasoline in fire debris using HS-SPME-GC/MS and transfer learning.

Due to the complex nature of the chemical compositions of ignitable liquids (IL) and the interferences from fire debris matrices, interpreting chromatographic data poses challenges to analysts. In this work, artificial intelligence (AI) was developed by transfer learning in a convolutional neural network (CNN), GoogLeNet. The image classification AI was fine-tuned to create intelligent classification systems to discriminate samples containing gasoline residues from burned substrates. All ground truth samples were analyzed by headspace solid-phase microextraction (HS-SPME) coupled with a gas chromatograph and mass spectrometer (GC/MS). The HS-SPME-GC/MS data were transformed into three types of image presentations, that is, heatmaps, extracted ion heatmaps, and total ion chromatograms. The abundance and mass-to-charge ratios of each scan were converted into image patterns that are characteristic of the chemical profiles of gasoline. The transfer learning data were labeled as "gasoline present" and "gasoline absent" classes. The assessment results demonstrated that all AI models achieved 100 ± 0% accuracy in identifying neat gasoline. When the models were assessed using the spiked samples, the AI model developed using the extracted ion heatmap obtained the highest accuracy rate (95.9 ± 0.4%), which was greater than those obtained by other machine learning models, ranging from 17.3 ± 0.7% to 78.7 ± 0.7%. The proposed work demonstrated that the heatmaps created from GC/MS data can represent chemical features from the samples. Additionally, the pretrained CNN models are readily available in the transfer learning workflow to develop AI for GC/MS data interpretation in fire debris analysis.

Carbon nanotubes-assisted solid-phase microextraction for the extraction of gasoline in fire debris samples.

Gasoline is one of the most encountered ignitable liquids (IL) in fire debris analysis. The extraction of gasoline from fire debris samples presents challenges due to the complicated nature of multicomponent mixtures. This research work proposed a novel carbon nanotube-assisted solid phase microextraction (CNT-SPME) fiber coupled with gas chromatography and mass spectrometry (GC/MS) to determine gasoline residues for fire debris analysis. The CNT-SPME fiber was prepared by a sequential coating of polydopamine, epoxy, and CNTs on a stainless-steel wire. The extraction capabilities of the CNT-SPME fiber for gasoline and its major aromatic groups (xylenes, alkylbenzenes, indanes, and naphthalenes) from neat and spiked samples were promising, with linear dynamic ranges of 0.4-12.5 and 3.1-12.5 µg 20-mL-1 headspace vial, respectively. The average relative standard deviations and accuracies for all concentration ranges in this work were lower than 15%. The relative recovery of the CNT-SPME fiber for all aromatic groups ranged from 28 ± 3% to 59 ± 2%. Additionally, the CNT-SPME fiber showed a higher selectivity for the naphthalenes group in gasoline, as indicated by the experimental outcome using a pulsed thermal desorption process of the extracts. We envision the nanomaterial-based SPME offers promising opportunities for extracting and detecting other ILs to support fire investigation.

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 5 min 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.

Review: Recent advancements and moving trends in chemical analysis of fire debris.

This review describes recent advances and current trends in fire debris analysis from 2014 to 2021. Onsite analytical techniques used for fire scene investigation, identifying samples of interest for later analysis as well as onsite confirmatory techniques are examined. Laboratory techniques are reviewed both from a perspective of instrumentation and data analysis. Advances in analytical techniques include GC x GC-TOFMS, DART-MS, HS-GC-IMS. New and emerging methods of data analysis including those using machine learning are assessed. Each aspect is essential for forensic scientists to obtain the correct conclusion when collecting, examining, analysing, and interpreting fire debris. This review concludes that there is a need for the validity and certainty of all methods to be assessed if they are to be used to generate reports or draw conclusions.

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.

Rapid GC-MS as a Screening Tool for Forensic Fire Debris Analysis.

Techniques developed for the screening of forensic samples can be useful for increasing sample throughput and decreasing backlog in forensic laboratories. One such technique, rapid gas chromatography mass spectrometry (GC-MS), allows for fast sample screening (≈1 min) and has gained interest in recent years for forensic applications. This work focuses on the development of a method for ignitable liquid analysis using rapid GC-MS. A sampling protocol and temperature program were developed for the analysis of these volatile samples. Using the optimized method for analysis, the limits of detection for compounds commonly found in ignitable liquids ranged from 0.012 mg/mL to 0.018 mg/mL. Once the method was developed, neat ignitable liquids (i.e., gasoline and diesel fuel) were analyzed, and major components in each liquid were identified. The identification of major compounds in gasoline and diesel fuel in the presence of substrate interferences was then assessed through the analysis of simulated fire debris samples. Three different substrates were spiked with each ignitable liquid, burned, and analyzed. Major compounds in both liquids were identified using the total ion chromatograms, relevant extracted ion profiles, and deconvolution methods.

The most remarkable interference to gasoline identification from polystyrene-co-butadiene and the corresponding cause.

The identification of ILRs in fire investigations has attracted great attention for decades, and background at fire scenes caused complex interference on ILR identification by contributing characteristic compounds. Aiming at exploring the correlation between the interference extent to gasoline identification and chemical composition/structure, two polystyrene-butadiene rubbers (SBr) with typical styrene contents involving alkylbenzene in molecules were selected particularly. The free burning residues in the presence and absence of gasoline were collected and analyzed via gas chromatography-mass spectrometry. It is striking that SBr with typical styrene content caused the most remarkable interference to gasoline identification as far as reported since it is even impossible to be distinguished from gasoline through chromatography profiles. Additionally, the molecular structure together with the chemical composition influences the interference extent as well. To trace the source of the remarkable interference from SBr, polystyrene, polybutadiene, as well as one polystyrene-butadiene-styrene block copolymer, were picked particularly due to their specific chemical relations. The results of target compounds analysis on the corresponding combustion residues revealed that the remarkable interference of SBrs originated from the combination of 'styrene' and 'butadiene' by contributing different target compounds. The results provide further support for the proposal of the correlation of the interferents chemical compositions with the interference extent. Furthermore, this study provides important references for fire debris analysis by predicting the interference of different substrates on the basis of their chemical composition.

Development of retention time indices for comprehensive multidimensional gas chromatography and application to ignitable liquid residue mapping in wildfire investigations.

In this study, we introduce a simple three-step workflow for a universally applicable RI system, to be used in GC×GC analysis of ignitable liquid residue (ILR) for arson investigations. The proposed RI system applies a combination of two well-established GC RI systems: non-isothermal Kovats (K) index in the first dimension and Lee (L) index in the second dimension. The proposed KLI RI system showed very good correlations when compared with predicted values and existing RI systems (r2 = 0.97 in first dimension, r2 = 0.99 in second dimension) and was valid for a wide range of analyte concentrations and operational settings (coefficient of variance (CV) < 1% in first dimension, < 10% in second dimension). Using the KLI RI, an ILR classification contour map was created to assist with the identification of ILR types within ASTM E1618. The contour map was successfully applied to neat fuels and a fire scene sample, highlighting the application to wildfire investigation. Standardizing the RI process and establishing acceptable error margins allows the exploration and comparison of comprehensive data generated from GC×GC analysis of ILRs regardless of location, time, or system, further enhancing comprehensive and tenable chemometric analyses of samples. Overall, the KLI workflow was inexpensive, quick to apply, and user-friendly with its simple 3-step design.

Detecting Chemical Vapor Diffusion through Firefighter Turnout Gear.

Firefighters are exposed to burning materials that may release toxic partial combustion and pyrolysis products into the environment, including compounds listed as priority pollutants by the United States Environmental Protection Agency (EPA). A novel passive sampling dosimeter device containing firefighter turnout gear as a diffusion membrane and an activated charcoal strip (ACS) for volatile analyte collection was designed and used to monitor potential exposures of firefighters to volatile organic compounds. Solvent extracts from the ACS and turnout gear diffusion layer were analyzed using Gas Chromatography-Mass Spectrometry (GC-MS) to determine the diffusion of compounds from burned substrates through firefighter turnout gear and compound adsorption to the turnout gear. The compounds in these samples were identified using target factor analysis (TFA). An activated carbon layer (ACL) was added to the dosimeter between the turnout gear and the ACS. The presence of combustion and pyrolysis compounds identified on the ACS in the dosimeter was reduced.

Influence of thermal environment in fire on the identification of gasoline combustion residues.

Recent advances in fire investigation have engendered significant interest in fire debris analysis. Many factors in fire scenes, however, may interfere with the identification of ignitable liquid residues (ILRs). Generally, all ILRs suffer unavoidably from thermal destruction in fires. In contrast to weathering, the thermal effects on ILRs involve evaporation, thermal degradation and other chemical reactions. In order to study the influence of the thermal environment in fire scenes on the stability of target compounds for ILRs identification, gasoline combustion residues were reheated at different temperatures and analyzed by gas chromatography-mass spectrometry (GC-MS) systematically. The results showed that polycyclic aromatic hydrocarbons (PAHs) and indanes were more susceptible to thermal destruction, and could not be detected effectively after heating. On the other hand, alkylbenzenes and condensed ring aromatics were comparatively more stable. When the temperature rose to 600 °C, almost all the target compounds were lost after reheating again for 2 min. The research provides an important reference for gasoline combustion residues identification, and care should be taken on the interpretation of results due to the inevitable thermal damage to ILRs in fires.

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

Recent research efforts in the domain of fire debris analysis have been mainly oriented towards the development of innovative analytical procedures and chemometric approaches for the detection and classification of ignitable liquids in fire specimens according to the ASTM E1618. However, less attention has been brought to the question of the source inference of ignitable liquids. Infer the identity of source of ignitable liquids recovered from arson sites is still a challenging and ongoing research area. In this study, the objective is to link neat gasoline samples sharing a common source through the use of an untargeted chemometric approach applied to data acquired by automated thermodesorption (ATD)-GC-MS following passive headspace extraction onto Tenax TA tubes. To that end, 190 unique gasoline samples from 19 gas stations collected over a year were used. A general and automated chemometric methodology for data treatment involving the following main steps is proposed: feature detection, normalization by exhaustive calculation of ratios between areas of pairs of features and selection of most discriminant ratios. The ratio selection procedure used here is based on the calculation of similarity measurements between pairs of samples sharing a common source or not. The algorithm maximizes the separation of the distributions of similarity measurements for related and unrelated samples by selecting a subset of ratios maximizing the area under the Receiver Operating Characteristics curve. The approach presented here was successfully applied to neat gasoline samples in order to assess if two gasoline samples share a common source or not.

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.

Total Ion Spectra versus Segmented Total Ion Spectra as Preprocessing Tools for Gas Chromatography - Mass Spectrometry Data.

Alignment of fire debris data from GC-MS for chemometric analysis is challenged by highly variable, uncontrolled sample and matrix composition. The total ion spectrum (TIS) obviates the need for alignment but loses all separation information. We introduce the segmented total ion spectrum (STIS), which retains the advantages of TIS while retaining some retention information. We compare the performance of STIS with TIS for the classification of casework fire debris samples. TIS and STIS achieve good model prediction accuracies of 96% and 98%, respectively. Baseline removal improved model prediction accuracies for both TIS and STIS to 97% and 99%, respectively. The importance of maintaining some chromatographic information to aid in deciphering the underlying chemistry of the results and reasons for false positive/negative results was also examined.

An analytical method to identify traces of white phosphorus on burned victim clothes.

We report for the first time, the chemical identification of phosphorus on the remains of burned clothes taken from an injured woman. The woman was accidentally burned as a result of spontaneous combustion of a "stone" pebble-like material her daughter picked up innocently on a beach. The remains of the woman's clothes were analyzed by gas chromatograph mass spectrometer (GCMS) after headspace adsorption using solid phase microextraction (SPME). The results of this test showed that the injuries were due to phosphorus, leading to the understanding that the "stone" was actually white phosphorus. This method can help both forensic investigators in a crime scene investigation and physicians that need this information in order to give the correct treatment to their patient.

Application of an HS-MS for the detection of ignitable liquids from fire debris.

In arson attacks, accelerants such as ignitable liquids are commonly used to initiate or accelerate a fire. The detection of ignitable liquid residues at fire scenes is therefore a key step in fire investigations. The most widely used analytical technique for the analysis of accelerants is GC-MS. However, pre-concentration of the ignitable liquid residues is required prior to the chromatographic analysis. The standard method, ASTM E1412, involves passive headspace concentration with activated charcoal strips as a method to isolate the ignitable liquid residues from fire debris and these residues are subsequently desorbed from the carbon strip with solvents such as carbon disulfide. In the work described here, an alternative analytical technique based on an HS-MS (headspace mass spectrometry) has been developed for the thermal desorption of the carbon strips and analysis of different ignitable liquid residues in fire debris. The working conditions for the HS-MS analytical procedure were optimized using different types of fire debris (pine wood burned with gasoline and diesel). The optimized variables were desorption temperature and desorption time. The optimal conditions were 145°C and 15 min. The optimized method was applied to a set of fire debris samples. In order to simulate post burn samples several accelerants (gasoline, diesel, citronella, kerosene, paraffin, and alcohol) were used to ignite different substrates (wood, cotton, cork, paper, and paperboard). chemometric methods (cluster analysis and discriminant analysis) were applied to the total ion spectrum obtained from the MS (45-200 m/z) to discriminate between the burned samples according to the accelerant used. The method was validated by analyzing all samples by GC-MS according to the standard methods ASTM E1412 and ASTM E1618. The results obtained on using the method developed in this study were comparable to those obtained with the reference method. However, the newly developed HS-MS method is faster, safer, and more environmental friendly than the standard method.

Analysis of arson fire debris by low temperature dynamic headspace adsorption porous layer open tubular columns.

In this paper we present results of the application of PLOT-cryoadsorption (PLOT-cryo) to the analysis of ignitable liquids in fire debris. We tested ignitable liquids, broadly divided into fuels and solvents (although the majority of the results presented here were obtained with gasoline and diesel fuel) on three substrates: Douglas fir, oak plywood and Nylon carpet. We determined that PLOT-cryo allows the analyst to distinguish all of the ignitable liquids tested by use of a very rapid sampling protocol, and performs better (more recovered components, higher efficiency, lower elution solvent volumes) than a conventional purge and trap method. We also tested the effect of latency (the time period between applying the ignitable liquid and ignition), and we tested a variety of sampling times and a variety of PLOT capillary lengths. Reliable results can be obtained with sampling time periods as short as 3min, and on PLOT capillaries as short as 20cm. The variability of separate samples was also assessed, a study made possible by the high throughput nature of the PLOT-cryo method. We also determined that the method performs better than the conventional carbon strip method that is commonly used in fire debris analysis.

Recovery of oxygenated ignitable liquids by zeolites, Part II: Dual-mode heated passive headspace extraction.

Previous studies performed by our research group have suggested that zeolites are a suitable adsorbent for the recovery of oxygenates from fire debris through heated passive headspace extraction. Zeolite 13X, in particular, has been shown to be effective for recovering analytes with molecular diameters smaller than 10Å. The primary aim of this study was to evaluate the addition of zeolite 13X to heated headspace extraction for the recovery of ignitable liquids. Comparative recoveries of petroleum and alcohol-based ignitable liquid mixtures were studied utilizing activated charcoal strips and zeolites, individually and in tandem. In the presence of both adsorption media within the same sample can, activated charcoal strips recovered the majority of gasoline components, while zeolites recovered the majority of oxygenated compounds. This phenomenon was attributed to the size exclusion properties, polarity, and available surface area of zeolites. This research supports the use of zeolites with activated charcoal strips in a "dual-mode" preparation for casework in which the presence of an ignitable liquid is suspected. The described method allows for the recovery and concentration of ignitable liquid residues in a single extraction procedure, whether the ignitable liquid is petroleum-based or oxygenated in nature.

Recovery of oxygenated ignitable liquids by zeolites, Part I: Novel extraction methodology in fire debris analysis.

The recovery of low molecular weight oxygenates in fire debris samples is severely compromised by the use of heated passive headspace concentration with an activated charcoal strip, as outlined in ASTM E-1412. The term "oxygenate" is defined herein as a small, polar, organic molecule, such as acetone, methanol, ethanol, or isopropanol, which can be employed as an ignitable liquid and referred to in the ASTM classification scheme as the "oxygenated solvents" class. Although a well accepted technique, the higher affinity of activated carbon strips for heavy molecular weight products over low molecular weight products and hydrocarbons over oxygenated products, it does not allow for efficient recovery of oxygenates such as low molecular weight alcohols and acetone. The objective of this study was to develop and evaluate a novel method for the enhanced recovery of oxygenates from fire debris samples. By optimizing conditions of the heated passive headspace technique, the utilization of zeolites allowed for the successful collection and concentration of oxygenates. The results demonstrated that zeolites increased the recovery of oxygenates by at least 1.5-fold compared to the activated carbon strip and may complement the currently used extraction technique.