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

Area of Science:

• Forensic Science
• Analytical Chemistry
• Chemical Analysis

Background:

• Ignitable liquid analysis in fire debris is crucial for fire investigations but traditionally time-consuming.
• Current methods involving passive headspace extraction present a bottleneck in reducing overall analysis workflow times.
• Rapid gas chromatography-mass spectrometry (GC-MS) offers a fast screening technique but is limited by extraction efficiency.

Purpose of the Study:

• To implement solid phase microextraction (SPME) with rapid GC-MS for a faster and more sensitive screening of ignitable liquids in fire debris.
• To compare the SPME-rapid GC-MS approach with traditional passive headspace extraction methods.
• To optimize the SPME-rapid GC-MS workflow for reduced analysis time and improved sensitivity.

Main Methods:

• Solid phase microextraction (SPME) was employed for the extraction of volatile compounds from fire debris samples.
• Rapid gas chromatography-mass spectrometry (GC-MS) was utilized for the instrumental analysis of extracted compounds.
• Optimized inlet conditions were established to enhance sensitivity and reduce detection limits.
• Deconvolution methods and extracted ion profiles (EIPs) were used for compound identification.

Main Results:

• The SPME-rapid GC-MS method achieved limits of detection as low as 27 ng/mL per compound.
• Gasoline and diesel fuel were successfully analyzed, with major compounds identified in chromatograms.
• The integrated workflow, from SPME extraction to data processing, was reduced to under 20 minutes per sample.
• The method demonstrated effectiveness on mock burn samples with carpet and wood substrates.

Conclusions:

• Solid phase microextraction (SPME) is a suitable injection technique for rapid GC-MS in fire debris analysis.
• The SPME-rapid GC-MS workflow provides a fast, sensitive, and efficient screening approach for forensic applications.
• This integrated method significantly reduces overall analysis time compared to traditional techniques.