In our study, we leveraged an electronic nose to detect the patterns of crude oils and their mixtures, sourced from the oil fields from neighboring regions in pursuit of the task of environmental impact evaluation. The temporal dynamics of oil-related patterns acquired by an electronic nose was tracked to identify the influence of high or low emissions of volatiles that depend on the oil composition. Analyzing the oils by Fourier-transform IR-spectroscopy and GC×GC-MS, we confirmed the correlation between sensor responses and the oil compositions, significantly dependent on the ratio of aromatic compounds/alkanes. Using pattern recognition techniques, Random Forest classifier enabled good accuracy of classification of oil samples and contaminated soils underscoring a high-resolution distinction between the response data. Applying these principles to determine the oil origin, we observed that the studied oil samples and contaminated soil samples corroborate with the dynamic changes in odor patterns based only on volatile and semivolatile compounds. Crude oils from the border of two oil fields facilitate a change in the odor pattern to remain one of the fields depending on the weathering time. These proposed intelligent multisensor systems show great promise as a tool for estimating oil-contaminated soils, thereby potentially enhancing environmental monitoring practices.

中文翻译：

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用电子鼻对原油的气味模式进行编码：土壤污染案例


在我们的研究中，我们利用电子鼻来检测来自邻近地区油田的原油及其混合物的模式，以追求环境影响评估的任务。跟踪电子鼻获得的与石油相关的模式的时间动态，以确定取决于石油成分的挥发物高排放或低排放的影响。通过傅里叶变换红外光谱和 GC×GC-MS 分析油，我们证实了传感器响应与油成分之间的相关性，这在很大程度上取决于芳香族化合物/烷烃的比例。使用模式识别技术，随机森林分类器能够很好地对油样和受污染土壤进行分类，从而强调响应数据之间的高分辨率区分。应用这些原则来确定石油来源，我们观察到，研究的油样和受污染的土壤样品与仅基于挥发性和半挥发性化合物的气味模式的动态变化相吻合。来自两个油田边界的原油有助于气味模式的变化，以保持油田之一，具体取决于风化时间。这些提出的智能多传感器系统作为估计受石油污染土壤的工具显示出巨大的前景，从而有可能加强环境监测实践。