Modern Instrumental Limits of Identification of Ignitable Liquids in Forensic Fire Debris Analysis

Forensic fire debris analysis is an important part of fire investigation, and gas chromatography– mass spectrometry (GC-MS) is the accepted standard for detection of ignitable liquids in fire debris. While GC-MS is the dominant technique, comprehensive two-dimensional gas chromatography–mass spectrometry (GC GC-MS) is gaining popularity. Despite the broad use of these techniques, their sensitivities are poorly characterized for petroleum-based ignitable liquids. Accordingly, we explored the limit of identification (LOI) using the protocols currently applied in accredited forensic labs for two 75% evaporated gasolines and a 25% evaporated diesel as both neat samples and in the presence of interfering pyrolysate typical of fire debris. GC-MSD (mass selective detector (MS)), GC-TOF (time-of-flight (MS)), and GC GC-TOF were evaluated under matched conditions to determine the volume of ignitable liquid required on-column for correct identification by three experienced forensic examiners performing chromatographic interpretation in accordance with ASTM E1618-14. GC-MSD provided LOIs of ~0.6 pL on-column for both neat gasolines, and ~12.5 pL on-column for neat diesel. In the presence of pyrolysate, the gasoline LOIs increased to ~6.2 pL on-column, while diesel could not be correctly identified at the concentrations tested. For the neat dilutions, GC-TOF generally provided 2 better sensitivity over GC-MSD, while GC GC-TOF generally resulted in 10 better sensitivity over GC-MSD. In the presence of pyrolysate, GC-TOF was generally equivalent to GC-MSD, while GC GC-TOF continued to show 10 greater sensitivity relative to GC-MSD. Our findings demonstrate the superior sensitivity of GC GC-TOF and provide an important approach for interlaboratory benchmarking of modern instrumental performance in fire debris analysis

COMPARING GC×GC-TOFMS-BASED METABOLOMIC PROFILING AND WOOD ANATOMY FOR FORENSIC IDENTIFICATION OF FIVE MELIACEAE (MAHOGANY) SPECIES

Illegal logging and associated trade have increased worldwide. Such environmental crimes represent a major threat to forest ecosystems and society, causing distortions in market prices, economic instability, ecological deterioration, and poverty. To prevent illegal imports of forest products, there is a need to develop wood identification methods for identifying tree species regulated by the Convention on International Trade in Species of Wild Fauna andFlora in Trade (CITES) and other look-alike species. In this exploratory study, we applied metabolomic profiling of five species (Swietenia mahagoni, Swietenia macrophylla, Cedrela odorata, Khaya ivorensis, and Toona ciliata) using two-dimensional gas chromatog- raphy combined with time-of-flight mass spectrometry (GC3GC-TOFMS). We also performed qualitative, quantitative (based on the measurement of vessel area, tangential vessel lumina diameter,vessel element length, ray height, and ray width), and machine-vision aided (XyloTron) wood anatomy on a subsample of wood specimens to explore thepotential and limits of each approach. Fifty dried xylaria wood specimens were ground, extracted with methanol, and subsequently analyzed by GC3GC-TOFMS. In this study, the four genera could easily be identified using qualitative wood anatomy and chemical profiling. At the spe- cies level, Swietenia macrophylla and Swietenia mahagoni specimens were found to share many major metabolites and could only be differentiated after feature selection guided by cluster resolution (FS-CR) and visualization using Principal Component Analysis (PCA). Expectedly, specimens from the two Swiete- nia spp. could not be distinguished based on qualitative wood anatomy. However, significant differences in quantitative anatomical features were obtained for these two species. Excluding T. ciliata that was not included in the reference database of end grain images at the time of testing (2021), the XyloTron could successfully identify the majority of the specimens to the right genus and 50% of the specimens to the right species. The machine-vision tool was particularly successful at identifying Cedrela odorata samples, where all samples were correctly identified. Despite the limited number of specimens available for thisstudy, our preliminary results indicate that GC3GC-TOFMS-based metabolomic profiles could be used as comple- mentary method to differentiate CITES-regulated wood specimens at the genus and species levels.

Comprehensive characterization of mainstream marijuana and tobacco smoke

Abstract: Recent increases in marijuana use and legalization without adequate knowledge of the risks necessitate the characterization of the billions of nanoparticles contained in each puff of smoke. Tobacco smoke offers a benchmark given that it has been extensively studied. Tobacco and marijuana smoke particles are quantitatively similar in volatility, shape, density and number concentration, albeit with differences in size, total mass and chemical composition. Particles from marijuana smoke are on average 29% larger in mobility diameter than particles from tobacco smoke and contain 3.4× more total mass. New measurements of semi-volatile fractions determine over 97% of the mass and volume of the particles from either smoke source are comprised of semi-volatile compounds. For tobacco and marijuana smoke, respectively, 4350 and 2575 different compounds are detected, of which, 670 and 536 (231 in common) are tentatively identified, and of these, 173 and 110 different compounds (69 in common) are known to cause negative health effects through carcinogenic, mutagenic, teratogenic, or other toxic mechanisms. This study demonstrates striking similarities between marijuana and tobacco smoke in terms of their physical and chemical properties

Region of Interest Selection for GC×GC-MS Data using a Pseudo Fisher Ratio Moving Window with Connected Components Segmentation

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) data present several challenges for analysis largely because chemical factors drift along the chromatographic modes across different chromatographic runs, and there is frequently a lack of reliable molecular ion measurements with which to align data across multiple samples. Tensor decomposition techniques such as Parallel Factor Analysis (PARAFAC2/PARAFAC2×N) allow analysts to deconvolve closely eluting signals for quantitative and qualitative purposes. These techniques make relatively few assumptions about chromatographic peak shapes or the relative abundance of noise and allow for highly accurate representations of the underlying chemical phenomena using well-characterized and scrutinized principles of chemometrics. However, expert intervention and supervision is required to select appropriate Regions of Interest (ROI) and numbers of chemical components present in each ROI. We previously reported an automated ROI selection algorithm for GC-MS data in Giebelhaus et al. where we observed the ratio of the first and second eigenvalues within a moving window across the entire chromatogram. Here, we present an extension of this work to automatically detect ROIs in GC×GC-MS chromatograms, while making no assumptions about peak shape. First we calculate the probabilities of each acquisition being in a ROI, then apply connected components segmentation to discretize the regions of interest. For sparse chromatograms we found the algorithm detected spurious peaks. To address this, we implemented an iterative ROI selection process where we autoscaled the moving window to the standard deviation of the noise from the previous iteration. Using three user-defined parameters, we generated informative ROIs on a wide range of GC×GC-TOFMS chromatograms

Limits of Detection and Quantification in Comprehensive Multidimensional Separations. 1. A Theoretical Look

Comprehensive multidimensional separations (e.g., GC×GC, LC×LC, etc.) are increasingly popular tools for the analysis of complex samples, due to their many advantages, such as vastly increased peak capacity, and improvements in sensitivity. The most well-established of these techniques, GC×GC, has revolutionized analytical separations in fields as diverse as petroleum, environmental research, food and flavors, and metabolic profiling. Using multidimensional approaches, analytes can be quantified at levels substantially lower than those possible by one-dimensional techniques. However, it has also been shown that the modulation process introduces a new source of error to the measurement. In this work, we present the results of a study into the limits of quantification and detection (LOQ and LOD) in comprehensive multidimensional separations using GC×GC and the more popular “two-step” integration algorithm as an example. Simulation of chromatographic data permits precise control of relevant parameters of peak geometry and modulation phase. Results are expressed in terms of the dimensionless parameter of signal-to-noise ratio of the base peak (<i>S</i>/<i>N</i>_BP) making them transportable to any result where quantification is performed using a two-step algorithm. Based on these results, the LOD is found to depend upon the modulation ratio used for the experiment and vary between a <i>S</i>/<i>N</i>_BP of 10–17, while the LOQ depends on both the modulation ratio and the phase of the modulation for the peak and ranges from a <i>S</i>/<i>N</i>_BP of 10 to 50, depending on the circumstances

Determination of Hydrocarbon Group-Type of Diesel Fuels by Gas Chromatography with Vacuum Ultraviolet Detection

A GC-vacuum ultraviolet (UV) method to perform group-type separations of diesel range fuels was developed. The method relies on an ionic liquid column to separate diesel samples into saturates, mono-, di-, and polyaromatics by gas chromatography, with selective detection via vacuum UV absorption spectroscopy. Vacuum UV detection was necessary to solve a coelution between saturates and monoaromatics. The method was used to measure group-type composition of 10 oilsands-derived Synfuel light diesel samples, 3 Syncrude light gas oils, and 1 quality control sample. The gas chromatography (GC)-vacuum UV results for the Synfuel samples were similar (absolute % error of 0.8) to historical results from the supercritical fluid chromatography (SFC) analysis. For the light gas oils, discrepancies were noted between SFC results and GC-vacuum UV results; however, these samples are known to be challenging to quantify by SFC-flame ionization detector (FID) due to incomplete resolution between the saturate/monoaromatic and/or monoaromatic/diaromatic group types when applied to samples heavier than diesel (i.e., having a larger fraction of higher molecular weight species). The quality control sample also performed well when comparing both methods (absolute % error of 0.2) and the results agreed within error for saturates, mono- and polyaromatics