Comparison of portable and benchtop GC–MS coupled to capillary microextraction of volatiles (CMV) for the extraction and analysis of ignitable liquid residues

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.

Sampling and recovery of ignitable liquid residues (ILRs) from fire debris using capillary microextraction of volatiles (CMV) for on-site analysis

A new, fast, and ultra-sensitive headspace sampling method using the Capillary Microextraction of Volatiles (CMV) device is demonstrated for the analysis of ignitable liquid residues (ILRs) in fire debris. This headspace sampling method involves the use of a heated can (60°C) to aid in the recovery of volatile organic compounds (VOCs) from medium and heavy petroleum distillates. Our group has previously reported the utility of CMV to extract gasoline at ambient temperature in less than 5 min in the field. This work evaluates the recovery and analysis of low mass loadings (tens of ng) of VOCs from charcoal lighter fluid, kerosene, and diesel fuel. Nonane, decane, undecane, tridecane, tetradecane, and pentadecane were selected for evaluation of recovery to represent these ILR classes. The face-down heated can headspace sampling technique was compared to the previously reported, non-heated, paper cup headspace sampling technique. Mass recovery improvements of 50%-200% for five of the six target compounds in diesel fuel were achieved compared to the non-heated sampling method. The average relative standard deviation (reported as % RSD) between the replicate trials decreased from an average of 28% to 6% when using the heated can method. Ignitable liquids were spiked onto burned debris in a live burn exercise and sampled using the heated can and paper cup headspace sampling techniques. The heated sampling technique reported here, for the first time, demonstrates an effective extraction method that when coupled to a portable GC-MS instrument allows for a sampling and analysis protocol in the field in less than 30 min.

Review: Recent advancements and moving trends in chemical analysis of fire debris

This review describes recent advances and current trends in fire debris analysis from 2014 to 2021. Onsite analytical techniques used for fire scene investigation, identifying samples of interest for later analysis as well as onsite confirmatory techniques are examined. Laboratory techniques are reviewed both from a perspective of instrumentation and data analysis. Advances in analytical techniques include GC x GC-TOFMS, DART-MS, HS-GC-IMS. New and emerging methods of data analysis including those using machine learning are assessed. Each aspect is essential for forensic scientists to obtain the correct conclusion when collecting, examining, analysing, and interpreting fire debris. This review concludes that there is a need for the validity and certainty of all methods to be assessed if they are to be used to generate reports or draw conclusions.

Characterization of Basil Volatile Fraction and Study of Its Agronomic Variation by ASCA

Basil is a plant known worldwide for its culinary and health attributes. It counts more than a hundred and fifty species and many more chemo-types due to its easy cross-breeds. Each species and each chemo-type have a typical aroma pattern and selecting the proper one is crucial for the food industry. Twelve basil varieties have been studied over three years (2018–2020), as have four different cuts. To characterize the aroma profile, nine typical basil flavour molecules have been selected using a gas chromatography–mass spectrometry coupled with an olfactometer (GC–MS/O). The concentrations of the nine selected molecules were measured by an ultra-fast CG e-nose and Principal Component Analysis (PCA) was applied to detect possible differences among the samples. The PCA results highlighted differences between harvesting years, mainly for 2018, whereas no observable clusters were found concerning varieties and cuts, probably due to the combined effects of the investigated factors. For this reason, the ANOVA Simultaneous Component Analysis (ASCA) methodology was applied on a balanced a posteriori designed dataset. All the considered factors and interactions were statistically significant (p < 0.05) in explaining differences between the basil aroma profiles, with more relevant effects of variety and year.