Classification Of Ignitable Liquids Research Articles

Automatic detection and classification of ignitable liquids from GC–MS data of casework samples in forensic fire-debris analysis

Investigation of fire scenes, in order to determine whether arson was involved, presents major challenges to both the forensic investigator who collects fire debris samples and the forensic scientist who analyzes them. A gas chromatography – mass spectrometry (GC–MS) chromatogram from the sample is compared to chromatograms from known ignitable liquid (IL) standards, and the analyst determines whether there are sufficient similarities to justify a positive detection (the fire debris contains IL residue) and classification (which IL it is). We have employed the chemometric software OpenChrom to accomplish real-time automatic detection and classification of 374 fire debris samples from casework fire scenes. In the tested samples, full agreement was achieved in IL detection between the software and the forensic experts: in no case did the software classify an IL-containing sample as “no IL” (false negative) or a “no IL” sample as containing IL (false positive). Moreover, of the 236 samples designated by the experts as containing IL, all were similarly classified by the software either as “gasoline” or “petroleum distillates” (diesel fuel, lighter fluid etc.). This study demonstrates the potential of an off-the-shelf fire debris automatic IL-detection tool in a forensic laboratory setting using hundreds of casework samples. Employment of this tool may help increase the efficiency and objectivity of the forensic laboratory.

Development of Crime Scene Intelligence Using a Hand-Held Raman Spectrometer and Transfer Learning.

The classification of ignitable liquids, such as gasoline, is critical crime scene intelligence to assist arson investigations. Rapid field gasoline classification is challenging because the current forensic testing standard requires gas chromatography-mass spectrometry analysis of evidence in an accredited laboratory. In this work, we reported a new intelligent analytical platform for field identification and classification of gasoline evidence. A hand-held Raman spectrometer was utilized to collect Raman spectra of reference gasoline samples with various octane numbers. The Raman spectrum pattern was converted into image presentations by continuous wavelet transformation (CWT) to facilitate artificial intelligence development using the transfer learning technique. GoogLeNet, a pretrained convolutional neural network (CNN), was adapted to train the classification model. Six different classification models were also developed from the same data set using conventional machine learning algorithms to evaluate the performance of our new approach. The experimental results indicated that the pretrained CNN model developed by our new data workflow outperformed other models in several performance benchmarks, such as accuracy, precision, recall, F1, Cohen's Kappa, and Matthews correlation coefficient. When the transfer learning model was challenged with the data collected from weathered gasoline samples, the classifier could still offer 73 and 53% accuracy for 50 and 25% weathered gasoline samples, respectively. In conclusion, wavelet transforms combined with transfer learning successfully processed and classified complex Raman spectral data without feature engineering. We envision that this nondestructive, automated, and accurate platform will accelerate crime scene intelligence development based on evidence's chemical signatures detected by hand-held Raman spectrometers.

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.

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.

Review of Analytical Techniques for Arson Residues

Arson is a serious crime that affects society through cost, property damage, and loss of life. It is important that the methods and technologies applied by fire investigators in detection of evidence and subsequent analyses have a high degree of reliability, sensitivity, and be subject to rigorous quality control and assurance. There have been considerable advances in the field of arson investigation since the 1950s. Classification of ignitable liquids has been updated to include many new categories due to developments in the petroleum industry. Techniques such as steam or vacuum distillation and gas chromatography (GC) with flame ionization detection that may have been considered acceptable--even a benchmark--40 years ago, are nowadays generally disfavored, to the extent that their implementation may almost be considered as ignorance in the field. The advent of readily available mass spectrometric techniques has revolutionized the field of fire debris analysis, increasing the degree of sensitivity and discrimination possible considerably. Multi-dimensional GC--particularly GC x GC--while not yet widely applied, is rapidly gaining recognition as an important technique. This comprehensive review focuses on techniques and practices used in fire investigation, from scene investigation to analysis.

Aromatic Content in Medium Range Distillate ProductséPart 1: An Examination of Various Liquids

Classification of ignitable liquids in accordance with voluntary consensus-based standards published by ASTM International has become increasingly specific, relying upon both the chemical composition and the boiling point range of submitted ignitable liquids. This classification system includes among others, specific classes for distillates and dearomatized distillates. In this study, a variety of medium-range ignitable liquids were analyzed by gas chromatography-mass spectrometry. Several methods of data analysis were utilized to examine the relative aliphatic and aromatic contents in these liquids. Results show that commercially available products in the medium range exhibit a broad range of compositions with respect to the relative proportion of aliphatic and aromatic compounds and that some liquids may not be easily classified. This study demonstrates the importance of examining the proportion of aliphatics:aromatics for classifying such liquids and suggests guidelines for differentiating medium range distillates, dearomatized distillates, and blended products.

ASTM standards for fire debris analysis: a review

The American Society for Testing and Materials (ASTM) recently updated its standards E 1387 and E 1618 for the analysis of fire debris. The changes in the classification of ignitable liquids are presented in this review. Furthermore, a new standard on extraction of fire debris with solid phase microextraction (SPME) was released. Advantages and drawbacks of this technique are presented and discussed. Also, the standard on cleanup by acid stripping has not been reapproved. Fire debris analysts that use the standards should be aware of these changes.