23 research outputs found
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.
Recommended from our members
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
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><sub>BP</sub>) 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><sub>BP</sub> 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><sub>BP</sub> 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
PARAFAC2N: Coupled Decomposition of Multi-modal Data with Drift in N Modes
Reliable analysis of comprehensive two-dimensional gas chromatography -
time-of-flight mass spectrometry (GCGC-TOFMS) data is considered to be
a major bottleneck for its widespread application. For multiple samples,
GCGC-TOFMS data for specific chromatographic regions manifests as a 4th
order tensor of I mass spectral acquisitions, J mass channels, K modulations,
and L samples. Chromatographic drift is common along both the first-dimension
(modulations), and along the second-dimension (mass spectral acquisitions),
while drift along the mass channel and sample dimensions is for all practical
purposes nonexistent. A number of solutions to handling GCGC-TOFMS data
have been proposed: these involve reshaping the data to make it amenable to
either 2nd order decomposition techniques based on Multivariate Curve
Resolution (MCR), or 3rd order decomposition techniques such as Parallel Factor
Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic
drift along one mode, which has enabled its use for robust decomposition of
multiple GC-MS experiments. Although extensible, it is not straightforward to
implement a PARAFAC2 model that accounts for drift along multiple modes. In
this submission, we demonstrate a new approach and a general theory for
modelling data with drift along multiple modes, for applications in
multidimensional chromatography with multivariate detection
Classifying Crystal Structures of Binary Compounds AB through Cluster Resolution Feature Selection and Support Vector Machine Analysis
Partial
least-squares discriminant analysis (PLS-DA) and support
vector machine (SVM) techniques were applied to develop a crystal
structure predictor for binary AB compounds. Models were trained and
validated on the basis of the classification of 706 AB compounds adopting
the seven most common structure types (CsCl, NaCl, ZnS, CuAu, TlI,
β-FeB, and NiAs), through data extracted from Pearson’s
Crystal Data and ASM Alloy Phase Diagram Database. Out of 56 initial
variables (descriptors based on elemental properties only), 31 were
selected in as unbiased manner as possible through a procedure of
forward selection and backward elimination, with the quality of the
model evaluated by measuring the cluster resolution at each step.
PLS-DA gave sensitivity of 96.5%, specificity of 66.0%, and accuracy
of 77.1% for the validation set data, whereas SVM gave sensitivity
of 94.2%, specificity of 92.7%, and accuracy of 93.2%, a significant
improvement. Radii, electronegativity, and valence electrons, previously
chosen intuitively in structure maps, were confirmed as important
variables. PLS-DA and SVM could also make quantitative predictions
of hypothetical compounds, unlike semiclassical approaches. The new
compound RhCd was predicted to have the CsCl-type structure by PLS-DA
(0.669 probability) and, at an even stronger confidence level, by
SVM (0.918 probability). RhCd was synthesized by reaction of the elements
at 800 °C and confirmed by X-ray diffraction to adopt the CsCl-type
structure. SVM is thus a superior classification method in crystallography
that is fast and makes correct, quantitative predictions; it may be
more broadly applicable to help identify the structure of unknown
compounds with any arbitrary composition