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Compound-specific isotope analysis of diesel fuels in a forensic investigation

机译:法医调查中柴油化合物的特定化合物同位素分析

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Compound-specific isotope analysis (CSIA) offers great potential as a tool to provide chemical evidence in a forensic investigation. Many attempts to trace environmental oil spills were successful where isotopic values were particularly distinct. However, difficulties arise when a large data set is analyzed and the isotopic differences between samples are subtle. In the present study, discrimination of diesel oils involved in a diesel theft case was carried out to infer the relatedness of the samples to potential source samples. This discriminatory analysis used a suite of hydrocarbon diagnostic indices, alkanes, to generate carbon and hydrogen isotopic data of the compositions of the compounds which were then processed using multivariate statistical analyses to infer the relatedness of the data set. The results from this analysis were put into context by comparing the data with the δ13C and δ2H of alkanes in commercial diesel samples obtained from various locations in the South Island of New Zealand. Based on the isotopic character of the alkanes, it is suggested that diesel fuels involved in the diesel theft case were distinguishable. This manuscript shows that CSIA when used in tandem with multivariate statistical analysis provide a defensible means to differentiate and source-apportion qualitatively similar oils at the molecular level. This approach was able to overcome confounding challenges posed by the near single-point source of origin i.e. the very subtle differences in isotopic values between the samples.
机译:化合物特异性同位素分析(CSIA)作为法医调查中提供化学证据的工具具有巨大潜力。在同位素值特别不同的情况下,许多追踪环境石油泄漏的尝试都是成功的。但是,当分析大量数据并且样品之间的同位素差异很细微时,会出现困难。在本研究中,对涉及柴油盗窃案的柴油进行了区分,以推断出样品与潜在来源样品的相关性。这种歧视性分析使用了一组烃类诊断指标烷烃来生成化合物组成的碳和氢同位素数据,然后使用多元统计分析对其进行处理以推断数据集的相关性。通过将数据与从新西兰南岛不同地点获得的商用柴油样品中烷烃的δ13C和δ2H进行比较,将分析结果与实际情况进行了比较。根据烷烃的同位素特征,有人认为与柴油盗窃案有关的柴油是可区分的。该手稿显示CSIA与多变量统计分析结合使用时,提供了一种在分子水平上区分和来源定性相似油类的可靠手段。这种方法能够克服由单点起源引起的混淆挑战,即样品之间同位素值的非常细微的差异。

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