Peak-cognizant Signal Processing of Raw Instrument Signals to Quantify Environmental Weathering of Contaminants from the Deepwater Horizon Spill
Peak-cognizant Signal Processing of Raw Instrument Signals to Quantify Environmental Weathering of Contaminants from the Deepwater Horizon Spill
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DOI：
10.1109/ieeeconf38699.2020.9389289
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Bernice Kubicek;A. Gupta;Fabian MullerDahlberg;A. Zelenski;R. Wong;E. Overton
中科院分区：
文献类型：
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作者：
Bernice Kubicek;A. Gupta;Fabian MullerDahlberg;A. Zelenski;R. Wong;E. Overton
在这项工作中，我们提出了从深水地平线漏油事件中对原油环境风化的峰值认知量化。其核心思想是从原油样品的原始GC-MS信号中自动提取峰信息，并在基于图形的定量计算框架中表示不同峰的相对风化程度。我们还给出了对原始信号进行基线校正和信号归一化处理的结果。保留时间对准是通过首先通过确定信号内突出峰之间的保留时间漂移并将计算的漂移应用于风化油样来对准来源油来执行的。将五个风化油样找峰、验证和分组为一个源油样品，可以发现化合物伴生关系。我们以图形可视化的形式提供了初步结果，允许快速和准确地解释多环芳烃(PAH)中的风化化合物。提供的结果是用从深水地平线漏油中收集的显示不同程度风化的石油样本生成的。
In this work, we present peak-cognizant quantification of environmental weathering of crude oil from the from the Deepwater Horizon oil spill. The key idea is to autonomously extract peak information from raw gas chromatography-mass spectrometry (GC-MS) signals from crude oil samples, and represent the relative weathering of different peaks in a graph-based quantitative computational framework. We also present results from pre-processing the raw signals with baseline correction and signal normalization. Retention time alignment is performed by first aligning the source oil by determining the retention time drift between prominent peaks within the signals and applying the calculated drift to the weathered oil samples. Peak finding, validation, and grouping of the five weathered oil samples to a source oil sample allows compound associations to be discovered. We present preliminary results as graphical visualizations allowing for rapid and precise interpretation of weathering compounds within polycyclic aromatic hydrocarbons (PAH). Results presented were generated with oil samples showing different degrees of weathering collected from the Deepwater Horizon spill.