Forensic Applications of Gas Chromatography (Analytical Concepts in Forensic Chemistry)
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Additional file 8 : Section S5 gives the detailed specification of weights as a function of peak heights used in this work. Figure 4 illustrates topography partitioning for injection 4 MW post-spill sample in Table S1 using injection 15 from Eugene Island, Gulf of Mexico as the reference for direct cross-PTM comparison for different thresholds. The 34 injections compared span across 31 distinct oil samples that originate from 19 distinct sources.
Fourteen samples originate from the MW, source of the Deepwater Horizon disaster, including two pre-spill samples, and twelve post-spill samples collected at diverse locations after the Deepwater Horizon disaster, e. These samples were collected in areas well documented [ 11 , 25 ] to be heavily contaminated by the Deepwater Horizon disaster compared to the background.
We evaluate the cross-PTM score as a function of the peak ratio threshold across a diverse selection of injection pairs. We examine the robustness of intra-class match between injections of same origin against inter-class distinction between injection pairings from different origins. We also compare the strength of MW vs. Three consecutive injections from a non-Gulf of Mexico NIST sample originating in the Monterey area are also analyzed as an ideal intra-class case study, independent of any co-provenance bias with the Gulf of Mexico samples.
Figure 5 plots the average cross-PTM score as a function of peak ratio threshold across important comparison classes. Additional file 9 : Figure S6.
We note that consistently the intra-class match between MW injections is statistically higher than the inter-class score between MW and other Gulf of Mexico injections. In Fig. Each plot shows the average cross-PTM score taken over all possible pairings of injections for the corresponding comparison class e. NIST vs. Each plot shows the average cross-PCA score taken over all possible pairings of injections for the corresponding comparison class e. To provide a neutral baseline for best-case performance, we compare three NIST injections injections 19—21 in Table S1 , all of which were taken from the same sample of non-Gulf of Mexico origin.
The NIST injections were run consecutively under practically identical experimental conditions. We observe in Fig. In reality, cross-comparisons for source determination are made between injections from different samples that may have same origin but are not consecutive runs from the same physical sample. Thus we expect the NIST vs.
Majority of the peaks considered are non-target biomarkers only 38 target biomarkers present among over biomarkers considered and thus offer a nuanced cross-PTM interpretation that accounts for both target and non-target contributions to an oil fingerprint. From Table 1 we observe that the inter-class match between MW injection pairings is well within statistical range, i. We observe from Table 1 and Fig. Thus, the PTM approach combines target and non-target analysis to address multi-layered forensic questions regarding whether the injections are from the same sample, from different samples of same origin, from samples of different origin but similar locale, and so on as demonstrated above in our analysis based on a unique and diverse set of oil samples.
However, purely statistical methods limit interpretation to peak aggregates, and as such, cannot provide peak-level interpretation.
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Therefore, by design PCA and similar multivariate statistical methods are compound-agnostic and cannot provide quantitative comparison based on relative compound concentrations in two complex mixtures. So, even minor nuances between two sources can carry important information to help us separate them once they are extracted from two closely located regions.
This differentiation between the two interpretation methods can be easily seen in Table 1 , where we compare the best performance for differentiating between GoM oil sources using PTM and PCA cross-comparison scores.
Forensic applications of gas chromatography
The optimal parameter choice for each method is provided number of components for PCA and peak ratio threshold for PTM. The intra-class match MW vs. Mathematically, we can perform PCA cross-comparison between these correlated courses based on the non-dominant components, but these are typically vulnerable to baseline noise and other uncertainties, and as such, not reliable for robust source differentiation.
This is evident in Fig. MW match. On the other hand, cross-PTM match scores Fig. For the sake of completeness, we summarize below our main findings from the simulation experiment and reproduce some related discussion. Specifically, we observed that despite expected increase in intra-class e. MW vs. MW matching error as perturbation is increased, the inter-class match e.
Macondo vs. For example, increasing statistical perturbation of peak locations from five pixels to ten pixels in the second dimension and introducing perturbation by unit pixel in the first dimension reduces the inter-class Macondo vs.
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However, the inter-class match scores MW vs. It is easy to see that despite the reduction in inter-class match due to increased perturbations, intra-class MW vs. MW comparisons. For example, the natural seep field sample was indistinguishable statistically from the Macondo class regardless of perturbation limits. PCA also exhibits much lower sensitivity to perturbations in the peak locations, which is to be expected, as it is a purely statistical compound-agnostic technique that does not consider one peak at a time.
We note that the contrast between PCA and PTM observed over simulations is consistent with that observed over the field data. This allows potential discovery of hitherto unknown connections between biomarkers that are related through topographic similarity between samples. The method proposed in this work is designed towards peak-to-peak comparisons, where each peak is distinctly formed and uniquely compared between samples refer Additional file 6 : Algorithm 1, Section S3. Therefore, the PTM method presented here is limited it its cluster-level interpretation, i.
Moreover, significant co-elution of smaller non-target peaks can lead to imprecise identification of cluster content and intra-cluster distributions using the peak-based PTM technique proposed here. Nonetheless, there is potential to extend the idea of peak topography mapping towards clustered interpretation, combining similar peak groups as one feature.
Some exploratory research with preliminary results regarding clustered interpretation and feature compression using peak pattern maps and manifold clusters is reported in [ 37 — 39 ]. It is out of scope for this work to examine compound clustering behavior and patterns derived thereof in detail, and deeper investigations are ongoing on whether the PTM method can be extended as a robust technique for knowledge discovery at the cluster level.
We also wish to iterate that the PTM method has been developed in this work with recalcitrant biomarkers in mind.
Michelle Groves Carlin and John R. Dean: Forensic applications of gas chromatography
It certainly has the potential to apply beyond the recalcitrant hopane-sterane biomarker region, for other crude oils and distillates, by measuring changes in peak heights of the same compounds across samples collected at different times and locations. Ideally, we believe we will be able to quantify weathering processes, subtract them from the signal, and continue to make highly quantitative comparisons.
However, such investigations warrant their own detailed study and as such, are outside the scope of this paper. We have validated our methods against experimental field data containing a diverse portfolio of oil samples across the world, with particular emphasis on the MW well, the source of Deepwater Horizon disaster, as well as over extensive perturbation analysis using numerical simulations Additional files 10 , Jolliffe I Principal component analysis.
Hoboken, Wiley. Wang Z, Fingas M Differentiation of the source of spilled oil and monitoring of the oil weathering process using gas chromatography-mass spectrometry. J Chromatogr A 2 — J Chromatogr A — Englewood Cliffs, Prentice Hall. Trends Environ Anal Chem — Environ Sci Technol 33 12 — Environ Forensics Standard practice for oil spill source identification by gas chromatography and positive ion electron impact low resolution mass spectrometry. Modified EPA The student resources previously accessed via GarlandScience.
Resources to the following titles can be found at www. Several areas of forensic science use the technique of gas chromatography, ranging from fire analysis to the investigation of fraudulent food and perfumes. Covering the essentials of this powerful analytical technique, Forensic Applications of Gas Chromatography explains the theory and shows Chromatography has many roles in forensic science, ranging from toxicology to environmental analysis. In particular, high-performance liquid chromatography HPLC is a primary method of analysis in many types of laboratories.
A forensic chemist is someone who is called in to analyze non-biological trace evidence found at crime scenes to identify unknown materials and match samples to known substances.
A forensic chemist generally works in a lab and is hired by the government, whether it be local, state, or federal. While in the lab they run tests on samples that have been collected by investigators. Some techniques that they use are optical analysis and gas chromatography.
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These techniques play a role in the investigation. Ultraviolet UV spectrometry helps distinguish between samples of proteins and nucleic acids such as deoxyribonucleic acid DNA. Infrared spectrophotometry is especially useful for the identification of organic compounds as bonds between certain atoms readily absorb infrared radiation IR.