Rapid GC–MS as a screening tool for forensic fire debris analysis

Capistran, Briana A.; Sisco, Edward

doi:10.1016/j.forc.2022.100435

Cited by 8 publications

(10 citation statements)

References 9 publications

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“…This approach produces a small amount of separation between substances in a mixture while still being able to leverage EI‐MS spectral libraries. Quick GC‐MS is relatively new, so there is limited literature available demonstrating its effectiveness in a seized drug setting; however, an application note (Cuadra‐Rodriguez, 2019) and a publication (Capistran & Sisco, 2022) have proposed and validated approaches for seized drug screening and ignitable liquid analysis, respectively.…”

Section: Emerging Analytical Solutionsmentioning

confidence: 99%

Forensic seized drug analysis: Current challenges and emerging analytical solutions

Sisco

2023

WIREs Forensic Science

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Over the past decade, new challenges in seized drug analysis have pushed the limits of existing instrumentation and workflows. These challenges include increasing caseloads, complex samples containing previously unidentified substances, and evolving legal requirements. Traditional approaches to seized drug analysis—such as gas chromatography mass spectrometry, color tests, and Fourier transform infrared spectroscopy—can be insufficient for current challenges, motivating the community to explore new instrumentation or data analysis solutions. However, the road to validation, implementation, and adoption of new technologies and solutions can be arduous. In this overview, we discuss the major challenges facing the seized drug community that can be addressed by advances in analytical approaches and highlight some emerging techniques that have been or could be adopted by laboratories in the near‐term. We also discuss specific hurdles limiting the implementation and adoption of new techniques, and approaches to lower these barriers.This article is categorized under: Forensic Chemistry and Trace Evidence > Controlled and Emerging Drug Compounds Forensic Chemistry and Trace Evidence > Emerging Technologies and Methods Forensic Chemistry and Trace Evidence > Presentation and Evaluation of Forensic Science Output

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Section: Emerging Analytical Solutionsmentioning

confidence: 99%

Forensic seized drug analysis: Current challenges and emerging analytical solutions

Sisco

2023

WIREs Forensic Science

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“…Configuration of the rapid GC-MS attachment to the traditional GC-MS instrument is described in detail elsewhere [14]. Briefly, the attachment connects to the traditional benchtop GC and houses its own chromatography column, a DB-1ht QuickProbe GC column (2 m length x 0.25 mm outer diameter x 0.10 µm inner diameter, Agilent Technologies).…”

Section: Rapid Gc-ms and Traditional Gc-msmentioning

confidence: 99%

“…A rapid GC-MS temperature program previously developed for ignitable liquids was utilized for all analyses: initial temperature of 35 C, hold 10 s, ramp 2 C/s to 100 C, ramp 4 C/s to 280 C, hold 10 s [14]. The total analysis time was 98 s (1.63 min).…”

Section: Rapid Gc-ms and Traditional Gc-msmentioning

confidence: 99%

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

Capistran

2023

Preprint

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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 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.

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“…Arson cases commonly involve the use of flammable materials, such as ignitable liquids (ILs), to deliberately increase the growth of fires [1,2]. According to the Federal Emergency Management Agency (FEMA), there was a 32% increase in property damage for residential building intentional fires between 2012 and 2021 [3].…”

Section: Introductionmentioning

confidence: 99%

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

Huang,

Chung Yu

2024

Journal of Forensic Sciences

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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.

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Rapid GC–MS as a screening tool for forensic fire debris analysis

Cited by 8 publications

References 9 publications

Forensic seized drug analysis: Current challenges and emerging analytical solutions

Forensic seized drug analysis: Current challenges and emerging analytical solutions

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

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

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