Interpreting comprehensive two-dimensional gas chromatography using peak topography maps with application to petroleum forensics
Interpreting comprehensive two-dimensional gas chromatography using peak topography maps with application to petroleum forensics
复制标题
使用峰形图解释综合二维气相色谱并应用于石油法证学
作者：
Hamidreza Ghasemi Damavandi;A. Sen Gupta;R. Nelson;C. Reddy
背景全二维气相色谱 imes GC）$$（GC×GC）提供了复杂混合物中数百种化合物的高分辨率分离，从而为复杂的定量解释提供了前所未有的信息。我们在$$（GC）中利用这种复合多样性 imes GC）$$（GC×GC）地形图提供定量的化合物识别解释，超出了目标化合物分析与石油法医学的实际应用。我们专注于$$（GC （GC×GC）$$（GC×GC）地形的生物标志物烃类，藿烷和甾烷，因为它们通常是不受风化的。我们介绍了峰地形图（PTM）和地形分区技术，考虑一个显着更广泛和更多样化的范围内的目标和非目标生物标志物化合物相比，传统的方法，考虑约20个生物标志物的比例。具体而言，我们考虑了33-154个靶和非靶生物标志物的范围，其中注射内的最高峰与最低峰的比率范围为4.86至19.6（精确数字取决于单独注射的生物标志物多样性）。我们还提供了一个强大的定量措施，直接确定“匹配”之间的样本，而不需要训练数据集。 imes GC）$$（GC×GC）注入从不同的石油来源的投资组合，并提供定量比较性能与既定的统计方法，如主成分分析（PCA）。我们的数据集包括2010年深水地平线灾难后收集的各种样本，该灾难从马孔多井（MW）释放了约1.6亿加仑原油。在这次灾难后收集的样本与其他密切相关的来源进行PTM解释时，显示出统计学显著的匹配（99.23 ± 1.66），（99.23±1.66）%。PTM为基础的解释也提供了更高的区分密切相关，但不同的来源比使用PCA为基础的统计比较。除了基于该实验场数据的结果之外，我们还提供了PTM方法在数值模拟上的扩展扰动分析，该数值模拟在$$（GC）上引入了峰位置的随机变化。 imes GC）$$（GC×GC）MW泄漏前样品的生物标志物ROI图像（附加文件4：表S1中的样品$$#1$$#1）。我们比较了两个维度上的交叉PTM分数对峰位置变化的鲁棒性，并比较了同一组模拟图像上的PCA分析结果。模拟实验的详细描述和结果的讨论在附加文件1：第S8节中提供。结论我们提供了一个定量解释$$（GC）的峰认知信息框架 imes GC）$$（GC×GC）形貌。拟议的地形分析使$$（GC （GC×GC）$$（GC×GC）法证解释，同时包括聚集在目标峰周围的鲜为人知的非目标生物标志物的细微差别。这允许潜在地发现靶生物标志物和非靶生物标志物之间迄今未知的联系。
BackgroundComprehensive two-dimensional gas chromatography $$(GC imes GC)$$(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 $$(GC imes GC)$$(GC×GC) topography to provide quantitative compound-cognizant interpretation beyond target compound analysis with petroleum forensics as a practical application. We focus on the $$(GC imes GC)$$(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.ResultsWe validate our methods across 34 $$(GC imes GC)$$(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 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 $$(99.23 pm 1.66 ),\%$$(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 $$(GC imes GC)$$(GC×GC) biomarker ROI image of the MW pre-spill sample (sample $$#1$$#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.ConclusionsWe provide a peak-cognizant informational framework for quantitative interpretation of $$(GC imes GC)$$(GC×GC) topography. Proposed topographic analysis enables $$(GC imes GC)$$(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.