Document Type

Article

Peer Reviewed

1

Publication Date

11-28-2016

Journal/Book/Conference Title

Chemistry Central Journal

DOI of Published Version

10.1186/s13065-016-0211-y

Abstract

Background

Comprehensive two-dimensional gas chromatography(GC×GC)">(GC×GC) (GC×GC)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(GC×GC)">(GC×GC) (GC×GC)topography to provide quantitative compound-cognizant interpretation beyond target compound analysis with petroleum forensics as a practical application. We focus on the(GC×GC)">(GC×GC) (GC×GC)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 (GC×GC)">(GC×GC) (GC×GC)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 Deepwater Horizon 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 (99.23±1.66)%">(99.23±1.66)% (99.23±1.66)% 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 (GC×GC)">(GC×GC) (GC×GC) biomarker ROI image of the MW pre-spill sample (sample #1">#1 #1 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(GC×GC)">(GC×GC) (GC×GC)topography. Proposed topographic analysis enables(GC×GC)">(GC×GC) (GC×GC)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

OAfund, GC × GC, Chromatography, Principal component analysis, Multivariate statistics, Quantitative interpretation, Oil-spill forensics

Journal Article Version

Version of Record

Published Article/Book Citation

Chemistry Central Journal 2016 10:75 http://doi.org/10.1186/s13065-016-0211-y

Rights

© The Author(s) 2016

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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URL

https://ir.uiowa.edu/ece_pubs/4