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Experimental determination of isotope enrichment factors – bias from mass removal by repetitive sampling

机译:同位素富集因子的实验测定 - 重复采样去除质量的偏差

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摘要

Application of compound-specific stable isotope approaches often involves comparisons of isotope enrichment factors (ε). Experimental determination of ε-values is based on the Rayleigh equation, which relates the change in measured isotope ratios to the decreasing substrate fractions and is valid for closed systems. Even in well-controlled batch experiments, however, this requirement is not necessarily fulfilled, since repetitive sampling can remove a significant fraction of the analyte. For volatile compounds the need for appropriate corrections is most evident and various methods have been proposed to account for mass removal and for volatilization into the headspace. In this study we use both synthetic and experimental data to demonstrate that the determination of ε-values according to current correction methods is prone to considerable systematic errors even in well-designed experimental setups. Application of inappropriate methods may lead to incorrect and inconsistent ε-values entailing misinterpretations regarding the processes underlying isotope fractionation. In fact, our results suggest that artifacts arising from inappropriate data evaluation might contribute to the variability of published ε-values. In response, we present novel, adequate methods to eliminate systematic errors in data evaluation. A model-based sensitivity analysis serves to reveal the most crucial experimental parameters and can be used for future experimental design to obtain correct ε-values allowing mechanistic interpretations.
机译:化合物特异性稳定同位素方法的应用通常涉及同位素富集因子(ε)的比较。 ε值的实验确定基于瑞利方程,该方程将测得的同位素比的变化与降低的底物分数相关联,并且对于封闭系统有效。但是,即使在控制良好的批次实验中,也不一定满足此要求,因为重复采样可以去除很大一部分分析物。对于挥发性化合物,最明显的是需要适当的校正,并且已经提出了各种方法来解决质量去除和挥发到顶部空间的问题。在这项研究中,我们同时使用合成数据和实验数据来证明,即使在设计合理的实验装置中,根据当前的校正方法确定ε值也容易导致相当大的系统误差。应用不合适的方法可能会导致错误和不一致的ε值,导致对同位素分馏过程的误解。实际上,我们的结果表明,由不适当的数据评估引起的伪影可能会导致已发布的ε值的可变性。作为回应,我们提出了新颖,适当的方法来消除数据评估中的系统错误。基于模型的灵敏度分析可揭示最关键的实验参数,并可用于将来的实验设计以获得正确的ε值,从而可以进行机械解释。

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