ASTM E1618 Research Articles

Machine learning approaches over ion mobility spectra for the discrimination of ignitable liquids residues from interfering substrates

In arson fires, ignitable liquids (ILs) are frequently used to start combustion. For this reason, detecting IL residues (ILRs) at the fire scene is a key factor in fire investigation to determine whether a crime has been committed as well as to establish the modus operandi of the perpetrator. In the present study, the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) for the detection of ILRs in fire debris from complex matrices in combination with machine learning (ML) tools is proposed as an alternative to the traditional method, based on gas chromatography–mass spectrometry (GC-MS), described by the ASTM E1618 standard method. For this purpose, petroleum-derived substrates (vinyl, nylon, and polyester) and natural substrates (cotton, cork and linoleum) burned alone and with different ILs (gasoline, diesel, ethanol and charcoal starter with kerosene) were used. In addition, samples were taken at different times (0, 1, 6, 12, 24 and 48 h) after the fire was finished. The ion mobility sum spectrum (IMSS) of each sample was obtained and different ML algorithms were applied. The first derivative was performed at the IMSS, as well as a Savitzky-Golay filter. Hierarchical cluster analysis (HCA) revealed a clustering trend as a function of substrate and ILs used, where the studied sampling times did not affect the resulting clusters. The classification models for the detection of the presence of ILRs have high performance with an accuracy of 100% for support vector machines (SVM) and random forest model (RF), followed by linear discriminant analysis (LDA) with an accuracy of 86.67%. When discriminating the type of ILs used, the RF model obtained an accuracy of 100%, followed by the LDA with 97.22% and finally the SVM model with an accuracy of 93.06%. In addition, a simple web application has been developed where the trained models can be used, so any researcher can apply the method to detect ILRs in fire debris.

Method development for optimizing analysis of ignitable liquid residues using flow-modulated comprehensive two-dimensional gas chromatography

The abundance and composition of matrix compounds in fire debris samples undergoing ignitable liquid residue analysis frequently leads to inconclusive results, which can be diminished by applying comprehensive two-dimensional gas chromatography (GC × GC). Method development must be undertaken to fully utilize the potential of GC × GC by maximizing separation space and resolution.. The three main areas to consider for method development are column selection, modulator settings and parameter optimization. Seven column combinations with different stationary phase chemistry, column dimensions and orthogonality were assessed for suitability based on target compound selectivity, retention, resolution, and peak shapes, as well as overall peak capacity and area use. Using Box-Behnken design of experimentation (DoE), the effect of modulator settings such as flow ratio and loop fill capacity were evaluated using carbon loading potential, dilution effect, as well as target peak amplitude and skewing effect. The run parameters explored for parameter optimization were oven programming, inlet pressure (column flow rate), and modulation period. Comparing DoE approaches, Box-Behnken and Doehlert designs assessed sensitivity, selectivity, peak capacity, and wraparound; alongside target peak retention, resolution, and shape evaluation. Certified reference standards and simulated wildfire debris were used for method development and verification, and wildfire debris case samples scrutinized for method validation. The final method employed a low polarity column (5% diphenyl) coupled to a semi-polar column (50% diphenyl) and resulted in an average Separation Number (SN) exceeding 1 in both dimensions after optimization. Separation Numbers of 18.16 for first and 1.46 for second dimension without wraparound for compounds with at least four aromatic rings signified successful separation of all target compounds from varied matrix compositions and allowed for easy visual comparison of extracted ion profiles. Mass spectrometry (MS) was required during validation to differentiate ions where no baseline separation between target compounds and extraneous matrix compounds was possible. The resulting method was evaluated against ASTM E1618 and found to be an ideal routine analysis method providing great resolution of target compounds from interferences and excellent potential for ILR classification within a complex sample matrix.

Application of a kinetic model to predict extracted ion profiles for the identification of evaporated ignitable liquids

A kinetic model was previously developed in our laboratory to predict evaporation rate constants of compounds in ignitable liquids as a function of gas chromatographic retention index. The model is applied to the chromatogram of an unevaporated liquid and used to predict chromatograms corresponding to any evaporation level of the liquid. In previous work, accuracy in predicting total ion chromatograms (TICs) for gasoline and petroleum distillates was demonstrated. In this work, the model is applied for the first time to predict extracted ion profiles (EIPs) corresponding to different compound classes.Fifteen ignitable liquids representing five classes (isoparaffinic, naphthenic-paraffinic, aromatic, petroleum distillate, and gasoline) defined in ASTM E1618 were experimentally evaporated to 50–90% (v/v). The kinetic model was applied to predict TICs and EIPs corresponding to the alkane, cycloalkane, aromatic, indane, and polynuclear aromatic compound classes for each evaporated liquid. Strong correlation between experimental and predicted TICs and EIPs indicated the predictive accuracy of the model. Reference collections were then generated containing predicted TICs and EIPs for each liquid corresponding to evaporation levels ranging from 10 to 90%. The reference collections were used to identify the ignitable liquid class in samples of increasing complexity, based on maximum correlation between the experimental and predicted chromatograms. In all cases, the correct liquid class was identified, even in the presence of substrate interferences. Overall, this work demonstrates the potential of a kinetic model to generate TICs and EIPs as a practical tool for identification of ignitable liquids in fire debris samples.

Discrimination of Ignitable Liquid Residues in Burned Petroleum-Derived Substrates by Using HS-MS eNose and Chemometrics.

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.

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.

Novel method based on ion mobility spectrometry sum spectrum for the characterization of ignitable liquids in fire debris

The destructive nature of fire together with a variety of interfering products from pyrolysis or background compounds among others, still offer a challenge on the proper identification of ignitable liquid residues (ILRs) in fire investigations. Nowadays, analysts use chromatography-mass spectrometry to try and classify ignitable liquids (IL) into one of the classes in the American Standards Testing Material method (ASTM E1618). In this study, an alternative approach is proposed to such analysis of fire debris. The proposed method would be based on ion mobility spectrometry sum spectrum (IMSSS) from headspace analysis, in combination with pattern recognition tools (Linear Discriminant Analysis, LDA). Four different substrates (pinewood, cork, paper, and cotton sheet) were burnt with and without different ILs (gasoline, diesel, ethanol, and paraffin). According to LDA, 100% of fire debris samples were correctly classified for presence/absence and type of IL. A characteristic fingerprint for each ILR was created for quick discrimination. These results demonstrate the potential of using IMSSS for a fast, objective and easy interpretation of fire debris data. In addition, ion mobility spectrometry (IMS) presents some advantages over traditional techniques such as its real-time monitoring capability and its capacity to work at atmospheric pressure, which allow the development of portable devices that would perform the analysis at the fire scene.

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.

Model-effects on likelihood ratios for fire debris analysis

A simple method is introduced for assessing the evidentiary value of fire debris samples. The method relies on models built by random draws from a database of ignitable liquid and substrate pyrolysis samples. A stratified random draw from database records belonging to each ASTM E1618 ignitable liquid class and the substrate class is taken in proportion to a prescribed distribution. The likelihood ratios are estimated by direct calculation from a one-level Gaussian kernel density model based on the covariance structure and multivariate means resulting from each random draw. Multiple draws result in model-related variation in the calculated likelihood ratios. The method is demonstrated for 500 random draws with replacement based on three distributions. Ten-fold cross-validation is performed for each model, with subsequent testing on laboratory burn samples. The average cross-validated receiver operating characteristic areas under the curve were 0.959, 0.956 and 0.947, for the three distributions. The mean log (base 10) of the likelihood ratio (LLR) values for the laboratory test burns of gasoline on polyester carpet and carpet padding were in the range of 1.5 to −3.1 and were qualitatively consistent with the observed gasoline contributions to the total ion chromatograms for the samples. The LLR values for laboratory test burns of a petroleum distillate on polyester carpet and carpet padding, 1.7 to −3.3, were also in qualitative agreement with the observed chromatographic patterns. The calculated LLR values for laboratory burns of a set of polyester carpet and padding samples were in the range −0.9 to −6.7.

Biodegradation of representative ignitable liquid components on soil

A mixture containing 14 hydrocarbons representing six distinct compound types (e.g. normal alkanes, branched alkanes, cyclic alkanes, aromatics, polynuclear aromatics, and oxygenates) was deposited onto ninety grams of potting soil and allowed to remain for specified periods of time. Hydrocarbons remaining at the end of each time period were extracted by passive headspace concentration and analyzed by gas chromatography-mass spectrometry. No byproducts were observed to be formed or accumulate during biodegradation in this study. There was sizable loss of oxygenated compounds, normal alkanes, and toluene compared to poly-substituted aromatics, polynuclear aromatics, branched alkanes, and cyclic alkanes. The half-life for the cumulative loss of all components of the hydrocarbon mixture was calculated to be 3.15days. A half-life as short as 1.75days was determined for the integrated loss of a commercial ignitable liquid from the ASTM E1618 aromatic class ignitable liquid.

Validation of an HS-MS method for direct determination and classification of ignitable liquids

Fire debris analysis is a key step in fire investigation. Most of the analytical methods used for the analysis of ignitable liquid residues in fire debris require pre-concentration of the ignitable liquid residues prior to the chromatographic analysis. The standard method, ASTM E1412, involves a passive headspace concentration with activated charcoal strips to isolate the ignitable liquid residues from fire debris followed by a desorption procedure from the carbon strip with carbon disulfide.In this study a non-separative analytical method based on HS-MS eNose (headspace mass spectrometry electronic nose) in combination with chemometric tools, including hierarchical cluster analysis and linear discriminant analysis, has been used to detect and classify ignitable liquid residues in fire debris into the classes defined by the ASTM E1618 standard method. This method allows a direct analysis of fire debris in 10min and avoids the use of solvent and sorbents.The method was validated by analyzing a set of burned samples by GC–MS according to the standard methods ASTM E1412 and ASTM E1618. The results obtained on using the method proposed in this study were comparable to those obtained with the reference method. The correct classification rates for assigning ignitable liquid residues into ASTM E1618 classes were in the range of 90% for both methods. In comparison to the standard methods HS-MS does have specific advantages. Apart from the speed of the analysis and the fact that the sample does not require sample pre-concentration, this technique is also safer and ecofriendly since it does not use any solvents or produce any residues.

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 15min.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 (45–200m/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.

Survey of American (USA) gasolines (2008).

The regulations for gasoline's content vary depending on the time of year and physical location within the United States while the refinery and distribution system mixes product batches; this results in variability of content. ASTM E1618 requires both the aromatic and alkane EIP patterns of gasoline to compare with references. A survey was conducted by collecting gasoline from Florida to Oregon, from 85 to 93 octane. Samples were analyzed in accordance with ASTM E1618 in various states of evaporation. The range of differences found in the 90% evaporated alkane EIPs is presented and showed a continuum of response when the n-alkane response was compared with the branched alkane response. Similarly, the ratio of the alkane EIP to the aromatic EIP also showed a continuum of response at the 90% evaporated state. Gasoline samples with unusual characteristics are also discussed.

Chemometric classification of casework arson samples based on gasoline content

Detection and identification of ignitable liquids (ILs) in arson debris is a critical part of arson investigations. The challenge of this task is due to the complex and unpredictable chemical nature of arson debris, which also contains pyrolysis products from the fire. ILs, most commonly gasoline, are complex chemical mixtures containing hundreds of compounds that will be consumed or otherwise weathered by the fire to varying extents depending on factors such as temperature, air flow, the surface on which IL was placed, etc. While methods such as ASTM E-1618 are effective, data interpretation can be a costly bottleneck in the analytical process for some laboratories. In this study, we address this issue through the application of chemometric tools.Prior to the application of chemometric tools such as PLS-DA and SIMCA, issues of chromatographic alignment and variable selection need to be addressed. Here we use an alignment strategy based on a ladder consisting of perdeuterated n-alkanes. Variable selection and model optimization was automated using a hybrid backward elimination (BE) and forward selection (FS) approach guided by the cluster resolution (CR) metric.In this work, we demonstrate the automated construction, optimization, and application of chemometric tools to casework arson data. The resulting PLS-DA and SIMCA classification models, trained with 165 training set samples, have provided classification of 55 validation set samples based on gasoline content with 100% specificity and sensitivity.

Microbial degradation of ignitable liquids on building materials

Gasoline was added to moldy samples of unused building materials. The unused samples were allowed to sit at room temperature for 2, 4, 7, and 14 days. Each set of samples was extracted using passive headspace concentration and analyzed using gas chromatography–mass spectrometry. Microbial degradation of the gasoline pattern was observed in limited samples to an extent that could result in an inability to identify an ignitable liquid according to ASTM E1618. The degradation noted was largely consistent with the results of previous microbial studies involving soil.

Combined target factor analysis and Bayesian soft-classification of interference-contaminated samples: Forensic Fire Debris Analysis

A Bayesian soft classification method combined with target factor analysis (TFA) is described and tested for the analysis of fire debris data. The method relies on analysis of the average mass spectrum across the chromatographic profile (i.e., the total ion spectrum, TIS) from multiple samples taken from a single fire scene. A library of TIS from reference ignitable liquids with assigned ASTM classification is used as the target factors in TFA. The class-conditional distributions of correlations between the target and predicted factors for each ASTM class are represented by kernel functions and analyzed by Bayesian decision theory. The soft classification approach assists in assessing the probability that ignitable liquid residue from a specific ASTM E1618 class, is present in a set of samples from a single fire scene, even in the presence of unspecified background contributions from pyrolysis products. The method is demonstrated with sample data sets and then tested on laboratory-scale burn data and large-scale field test burns. The overall performance achieved in laboratory and field test of the method is approximately 80% correct classification of fire debris samples.

The Petroleum-Laced Background

Using passive headspace concentration (ASTM E-1412) and gas chromatographic/mass spectrometric (GC/MS) analysis as described in ASTM E-1618, the authors have studied the volatile components detectable in several kinds of otherwise uncontaminated substrata, including clothing, shoes, household products, building materials, paper products, cardboard, and adhesives. Due to the use of petroleum-derived liquids in the manufacture of these materials, it is frequently possible to detect the liquids, even when the products are several years old. These results point out the need for the use of comparison samples whenever possible.