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

Validation of a Rapid GC-MS Method for Forensic Seized Drug Screening Applications

With the lack of standardized validation protocols across the forensic chemistry community, validation of instrumentation can be a challenging and time-consuming task. However, this process is crucial to understanding the associated capabilities and limitations, especially for nascent technologies. Rapid GC-MS is one such emerging analytical technique being increasingly implemented in forensic laboratories due to its fast and informative screening capabilities. However, a full validation for forensic samples has yet to be published since its debut. This work presents the results of a comprehensive validation of a rapid GC-MS system for seized drug screening through the assessment of nine components: selectivity, matrix effects, precision, accuracy, range, carryover/contamination, robustness, ruggedness, and stability. Single- and/or multi-compound test solutions of commonly encountered seized drug compounds were used to assess method and system performance. Results met the designated acceptance criteria for a majority of components. For example, retention time and mass spectral search score % RSDs were ≤ 10 % for precision and robustness studies. Limitations were identified for components that did not meet the acceptance criteria (e.g., inability to differentiate some isomers). The study design is part of a larger validation package developed for rapid GC-MS that includes a validation plan and automated workbook. The template, available for adoption by laboratories, ultimately aims to reduce the barrier of implementation for rapid GC-MS technology.

Comprehensive Data Evaluation Methods Used in Developing the SWGDRUG Mass Spectral Reference Library for Seized Drug Identification

The mass spectral library of the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG) is the most comprehensive free reference database of its kind in the world. It provides reliable mass spectra for identification of seized drugs, their metabolites, and related forensic compounds when using gas chromatography/mass spectrometry (GC/MS). The SWGDRUG library (version 3.13) contains spectra for 3598 compounds. All spectra are evaluated by the Mass Spectrometry Data Center (MSDC) at the National Institute of Standards and Technology (NIST). Over the past few years, new evaluation methods aided by improved software have been developed. First, all chemical information, such as chemical structure and name, is confirmed. Second, the product ions in each spectrum are verified to match the compound structure using the NIST MS Interpreter software tool. Subsequently, the mass spectra are compared to the same or similar compounds across six different mass spectral reference libraries using three distinct library search methods. Additionally, the NIST Artificial Intelligence Retention Indices (AIRI) software is used to help confirm the corresponding compounds of spectra, especially for those without molecular ions. Low-quality and incorrect spectra are rejected for inclusion in the library.

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.

Implementation of SPME and Rapid GC-MS as a Screening Approach for Forensic Fire Debris Applications

Analysis of ignitable liquids in fire debris samples can be a time-consuming process, from extraction of volatile compounds to instrumental analysis. Rapid gas chromatography-mass spectrometry (GC-MS) is a screening technique that can be utilized prior to confirmatory GC-MS analysis to provide an informative screening approach and possibly reduce the need to further analyze negative samples. Though rapid GC-MS is fast (less than two minutes), extraction techniques such as passive headspace extraction remain a bottleneck for decreasing overall workflow times. In this work, solid phase microextraction (SPME) was implemented with rapid GC-MS for ignitable liquid analysis for a faster, more sensitive screening approach compared to extraction with passive headspace. Using optimized inlet conditions, limits of detection as low as 27 ng/mL per compound were achieved. Gasoline and diesel fuel were extracted and analyzed, and major compounds in each liquid were identified in the resulting chromatograms. Extracted ion profiles (EIPs) and deconvolution methods were useful for additional compound identifications. Lastly, the SPME-rapid GC-MS workflow was extended to the analysis of gasoline and diesel fuel in mock burn samples using carpet and wood substrates. From SPME sample extraction to rapid GC-MS instrumental analysis and data processing, the total workflow for a single sample was reduced to under 20 min. These results indicate that SPME is a suitable injection technique for rapid GC-MS to provide a fast and sensitive screening approach for fire debris applications.