Interpreting comprehensive two-dimensional gas chromatography using peak topography maps with application to petroleum forensics

Abstract

Background: Comprehensive two-dimensional gas chromatography [Formula: see text] provides high-resolution separations across hundreds of compounds in a complex mixture, thus unlocking unprecedented information for intricate quantitative interpretation. We exploit this compound diversity across the [Formula: see text] topography to provide quantitative compound-cognizant interpretation beyond target compound analysis with petroleum forensics as a practical application. We focus on the [Formula: see text] topography of biomarker hydrocarbons, hopanes and steranes, as they are generally recalcitrant to weathering. We introduce peak topography maps (PTM) and topography partitioning techniques that consider a notably broader and more diverse range of target and non-target biomarker compounds compared to traditional approaches that consider approximately 20 biomarker ratios. Specifically, we consider a range of 33-154 target and non-target biomarkers with highest-to-lowest peak ratio within an injection ranging from 4.86 to 19.6 (precise numbers depend on biomarker diversity of individual injections). We also provide a robust quantitative measure for directly determining "match" between samples, without necessitating training data sets.

Results: We validate our methods across 34 [Formula: see text] injections from a diverse portfolio of petroleum sources, and provide quantitative comparison of performance against established statistical methods such as principal components analysis (PCA). Our data set includes a wide range of samples collected following the 2010 DeepwaterHorizon disaster that released approximately 160 million gallons of crude oil from the Macondo well (MW). Samples that were clearly collected following this disaster exhibit statistically significant match [Formula: see text] using PTM-based interpretation against other closely related sources. PTM-based interpretation also provides higher differentiation between closely correlated but distinct sources than obtained using PCA-based statistical comparisons. In addition to results based on this experimental field data, we also provide extentive perturbation analysis of the PTM method over numerical simulations that introduce random variability of peak locations over the [Formula: see text] biomarker ROI image of the MW pre-spill sample (sample [Formula: see text] in Additional file 4: Table S1). We compare the robustness of the cross-PTM score against peak location variability in both dimensions and compare the results against PCA analysis over the same set of simulated images. Detailed description of the simulation experiment and discussion of results are provided in Additional file 1: Section S8.

Conclusions: We provide a peak-cognizant informational framework for quantitative interpretation of [Formula: see text] topography. Proposed topographic analysis enables [Formula: see text] forensic interpretation across target petroleum biomarkers, while including the nuances of lesser-known non-target biomarkers clustered around the target peaks. This allows potential discovery of hitherto unknown connections between target and non-target biomarkers.

Keywords: Chromatography; GC × GC; Multivariate statistics; Oil-spill forensics; Principal component analysis; Quantitative interpretation.

Fig. 1

a The three-dimensional view of detailed topography of biomarker region (hopanes and steranes) within $GC\times GC$ image of crude oil pre-spill sample from MW, site of Deepwater Horizon spill disaster, Gulf of Mexico, 2010. b Biomarker region (hopanes and steranes) of (a) marked as the region of interest (ROI), shown as red box within full chromatogram.Target biomarkers within this ROI are labeled and itemized in Table S2. Total number of detected biomarker peaks (target and non-target) = 111, after removing peaks occupying lowest 5 % of the $GC\times GC$ peak magnitude profile as baseline noise. Range of considered peak summits (highest:lowest) = 14.53:1 (Aeppli et al. [25] Nelson et al. [36])

Fig. 2

Step-by-step PTM construction Target biomarkers are labeled and itemized in Table S2. Total number of detected biomarker peaks (target and non-target) = 111, after removing peaks occupying lowest $5\%$ of the $GC\times GC$ peak magnitude profile as baseline noise. Range of considered peak summits (highest:lowest) = 14.53:1

Fig. 3

a The three-dimensional view of $GC\times GC$ image of crude oil sample from Eugene Island, Gulf of Mexico, about 50 miles southwest of MW, the oil source of the Deepwater Horizon disaster. b The two-dimensional view of the full chromatogram, with yellow box showing region of interest (hopanes and steranes) detailed in a. c Two-dimensional view of detailed topography of biomarker region (hopanes and steranes) marked as yellow box in b.Target biomarkers are labeled and itemized in Table S2. d PTM representation of ROI shown as yellow box in b. Thirty-eight target biomarkers are allocated to the numerically labeled PTM nodes. Each PTM node is uniquely assigned to each peak and therefore, each target peak is uniquely identifiable against the non-target peaks

Fig. 4

Topography partitioning of injection 15 (Eugene Island, Gulf of Mexico) with reference injection 4 (post-spill sample taken from the broken riser pipe of MW) for peak ratio threshold a $\mathit{\tau }=1.3$ and b $\mathit{\tau }=1.65$

Fig. 5

Mean cross-PTM scores plotted as a function of the peak ratio threshold $\mathit{\tau }$ for important intra-class (same source) and inter-class (distinct sources) comparisons. Each plot shows the average cross-PTM score taken over all possible pairings of injections for the corresponding comparison class (e.g. NIST vs. NIST plot shows the average cross-PTM score for three possible parings between the three NIST injections). Macondo refers to any crude oil sample originating from the MW, source of the Deepwater Horizon disaster

Fig. 6

Mean cross-PCA scores plotted as a function of the peak ratio threshold $\mathit{\tau }$ for important intra-class (same source) and inter-class (distinct sources) comparisons. Each plot shows the average cross-PCA score taken over all possible pairings of injections for the corresponding comparison class (e.g. NIST vs. NIST plot shows the average cross-PCA score for three possible parings between the three NIST injections). Macondo refers to any crude oil sample originating from the MW, source of the Deepwater Horizon disaster