Ignitable Liquids Research Articles

Use of optimized 1H selTOCSY for identification and individualization of petrol samples from fire debris

Detection and identification of ignitable liquids in fire investigation is ongoing challenge for forensics investigators. This study uses the analytical power of NMR analysis and permits the identification of trace amounts of olefinic compounds the complex mixture of petrol that aids the individualization of petrol sources. The 1H selTOCSY NMR method has proven to be successful in the investigation of the four sets of alkenes namely: 3-methyl-1-butene (set 1), a mixture of 3-methyl-1-butene and 1-pentene (set 2), 2-methyl-2-butene (set 3) and a mixture of cis and trans-2-pentene (set 4) in petrol samples evaporated to different extents, simulating the action of weathering of actual casework samples, and still achieved significant discrimination of petrol brands at 25 % and 50 % evaporation. Discrimination of burned samples also proved possible, both of burnt petrol its own and then petrol burned on different substrates including wood, carpet, fabric, and paper materials representing common household fire debris residues. 1H selTOCSY NMR experiments on these alkene signals remove most of the non-alkene-related signals from the spectra and thus provide additional clarity in heavily burnt and contaminated samples, increasing the capacity to discriminate between samples using the four alkene markers.

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.

Pre-mixed small engine fuels (SEFs): Ignitable liquid analysis, canine detection, and a discussion of formulation changes

A variety of types and brands of pre-mixed small engine fuels (SEFs) were analyzed by gas chromatography-mass spectrometry (GC–MS) to determine their ignitable liquid composition. Additionally, many of these brands and fuel mixes were tested six years apart, first in 2018 and again in 2024, to determine if any formulation changes had occurred. All tested products were comprised of a range of isoparaffinic content, and most also contained at least one aromatic compound. One product marketed as a fuel treatment to fix ethanol-related issues contained 2-butoxyethanol. To determine Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) ignitable liquid detection canine (ILDC) response to the specific combination of ignitable liquids in these products, ILDC teams searched representative samples of the SEFs with no detection difficulty shown for the vast majority of these products. Reporting the ignitable liquid classification of SEFs would be dependent upon individual forensic science service provider (FSSP) protocols and the appearance of the ignitable liquid in casework data. The classification possibilities for these mixtures are discussed, including a case example of data resembling an SEF.

The effects of F-500 Encapsulator Agent on canine alerts and laboratory analysis using passive headspace concentration and gas chromatography-mass spectrometry

F-500 Encapsulator Agent (EA) is a fire suppression agent that is an alternative to traditional firefighting foams. It is marketed as having the capability to act on all four parts of the fire tetrahedron as well as being environmentally friendly and non-toxic. An internal survey of the use of F-500 EA by fire departments encountered by the Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) ignitable liquid detection canine (ILDC) handlers in 2022 showed that this product is not yet in widespread use across the country, but where it has been implemented, it is frequently utilized on a variety of types of fires. Additional agencies are researching the product to determine if it should be adopted. As this product appears to be growing in popularity, it is important to understand whether the use of the product would affect a canine’s ability to detect ignitable liquids or a forensic laboratory’s ability to identify the presence of an ignitable liquid. Burned wood and burned carpet, two commonly encountered substrates, were spiked with gasoline or a heavy petroleum distillate (HPD) and F-500 EA was applied. At various time intervals, ILDC teams surveyed the samples and laboratory analysis was conducted. Results showed that the presence of F-500 EA can negatively affect canine alerts and the laboratory’s ability to identify ignitable liquids.

Prediction of the influence of pressure on flash points of liquid fuels at sub-atmospheric pressure

Aircraft fuel tanks experience sub-atmospheric pressure during flight, and so do fuels during storage and transportation at high altitudes. Additionally, chemical processes commonly operate at non-atmospheric pressure. Flash points measured at sub-atmospheric pressure are lower than those measured at the standard atmospheric pressure of 101.3 kPa, signifying that ignitable liquids at sub-atmospheric pressure are more hazardous than those at 101.3 kPa. This study developed a model for predicting the influence of pressure on flash points on the basis of basic thermodynamic characteristics; this model was validated against experimental data obtained from the literature for six single-component and multiple-component liquid fuels at sub-atmospheric pressure. The proposed model effectively predicts closed-cup flash points, with small deviations in the range 0.18 °C–1.25 °C. However, because the model's assumption of vapor–liquid equilibrium was violated in the experiments, the predicted open-cup flash points did not agree well with their experimental counterparts, with the deviations in the range 1.11 °C–12.55 °C. Nevertheless, the trends predicted by the model agreed with those in the experimental data. Furthermore, standard flash point test method (such as ASTM D56-22, ASTMD93-20, and ASTMD7094-17) are based on a linear formula for correcting flash points measured at pressures other than 101.3 kPa. When the ambient pressure is approximately 101.3 kPa during flash point testing, the slope value of the correction formula should be changed from 0.25 to 0.20.

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.

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.

Optimization of a carbon adsorption/HS-GC–MS method for ignitable liquids classification from fire debris

The main goal of any fire investigation is to find the origin and cause of fire. Fire is commonly initiated or accelerated by using ignitable liquids (ILs). The analysis of fire debris can contribute to the determination of the origin of the fire. In this study, the thermal desorption of IL residues (ILRs) from activated charcoal strips (ACS) was optimized prior to headspace- gas chromatography- mass spectrometry (HS-GC–MS) analysis for the identification and classification of ILRs in fire debris samples. A Box-Behnken Design (BBD) coupled with Response Surface Methodology (RSM) were used to determine the optimized conditions in the following ranges: incubation temperature (80–140 ˚C), incubation time (30–60 min), and agitation speed (250–750 rpm). The sum of the differences between the total ion spectrum (TIS) of gasoline and diesel was used as dependent variables. The quadratic model was significant with a p- value of 0.0061 and a coefficient of determination equals to 0.911. RSM revealed that an increase in incubation temperature and agitation speed increases the response. The optimized conditions were 140 °C and 750 rpm. Further optimization of the incubation time was studied by analyzing fire debris samples at different times (5–30 min) and 5 min was determined as the optimum condition. The final optimized conditions were applied in combination with chemometrics for the characterization of fire debris samples burned with different ILs. The obtained results demonstrate the feasibility of the method in fire debris analysis which avoids the use of organic solvents providing a greener and faster alternative method.

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

Influence of chemical structure of SBr on the interference to gasoline identification for fire debris analysis

To examine the relationship between the molecular composition of interferents and their interference on gasoline identification in fire debris analysis, we conducted free burning experiments using five polystyrene-butadienes (SBrs) with styrene contents ranging from 9.5 wt% to 40 wt%. The combustion residues of these SBrs with and without the presence of gasoline were analyzed via gas chromatography-mass spectrometry. The compounds frequently used for gasoline identification, were detected in all the SBr combustion residues, and the Pearson product moment correlation coefficients have been calculated to quantify the interference extent based on the relative peak area ratios of the characteristic compounds. When gasoline is loaded onto these SBrs, particular attention should be paid since the relative content of the compounds of interest changed significantly, which distorted the chromatogram profile of gasoline greatly. Combining the above analysis results, we can confirm that materials containing “alkylbenzene” in their molecular structure are likely to result in both false positive and false negative identifications when determining the presence of ignitable liquid. Additionally, the interference extent varies with the molecular composition of the interferents. This further emphasizes the importance of considering the molecular structure when assessing the interference caused by these materials.

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.

Forensic Mass Spectrometry: Scientific and Legal Precedents.

Mass spectrometry has made profound contributions to the criminal justice system by providing an instrumental method of analysis that delivers exquisite analytical figures of merit for a wide variety of samples and analytes. Applications include the characterization of trace metal impurities in hair and glass to the identification of drugs, explosives, polymers, and ignitable liquids. This review describes major historical developments and, where possible, relates the developed capabilities to casework and legal precedents. This review also provides insight into how historical applications have evolved into, and out of, modern consensus standards. Unlike many pattern-based techniques and physical-matching methods, mass spectrometry has strong scientific foundations and a long history of successful applications that have made it one of the most reliable and respected sources of scientific evidence in criminal and civil cases. That said, in several appellate decisions in which mass spectrometric evidence was challenged but admitted, decisions sometimes still went against the mass spectrometric data anyway, which goes to show that mass spectrometric evidence is always just one piece of the larger legal puzzle.

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.

GC/MS and DART-MS as complementary methods for investigating the effects of weathering on chemical profiles of ignitable liquids: A case study for paint thinner

In this study, gas chromatography/mass spectrometry (GC/MS) and direct analysis in real time mass spectrometry (DART-MS) were used to analyze paint thinner samples weathered under different conditions. As GC/MS total ion chromatograms (TIC) of weathered paint thinner samples varied significantly due to the disparate evaporation rates of organic compounds in the mixture, DART-MS spectra have shown more consistent profiles among different samples. The distinctive ion clusters identified in the DART-MS spectra were attributed to the less volatile glycol ethers and polymeric compounds with alkyl chains in the paint thinner samples. Analysis of variance-principal component analysis (ANOVA-PCA) was applied to study the contributions of weathering degree and temperature factors to the total variance of GC/MS TIC and DART-MS spectral datasets and found that both factors played significant roles in the weathering process (p < 0.05), though weathering degree contributed to considerably more total variance to GC/MS TIC profiles (i.e., 93.4%). The paint thinner GC/MS and DART-MS profiles were compared with those from other common ignitable liquids, such as gasoline, torch fuel, lighter fluid, and Japan drier. The partial least squares discriminant analysis (PLS-DA) models were constructed to classify the ignitable liquids with the classification rates of 99.97 ± 0.02% and 99.80 ± 0.08% for models based on GC/MS and DART-MS data, respectively. Our study has shown that DART-MS can provide discriminative information for distinguishing ignitable liquid samples and complement the existing GC/MS method by the detection of less volatile compounds.