Woldegebriel Michael, Gonsalves John, van Asten Arian, Vivó-Truyols Gabriel
Analytical Chemistry, Van't Hoff Institute for Molecular Sciences, University of Amsterdam , P.O. Box 94720, 1090 GE Amsterdam, The Netherlands.
Netherlands Forensic Institute , P.O. Box 24044, 2490 AA The Hague, The Netherlands.
Anal Chem. 2016 Feb 16;88(4):2421-30. doi: 10.1021/acs.analchem.5b04484. Epub 2016 Jan 28.
As part of forensic toxicological investigation of cases involving unexpected death of an individual, targeted or untargeted xenobiotic screening of post-mortem samples is normally conducted. To this end, liquid chromatography (LC) coupled to high-resolution mass spectrometry (MS) is typically employed. For data analysis, almost all commonly applied algorithms are threshold-based (frequentist). These algorithms examine the value of a certain measurement (e.g., peak height) to decide whether a certain xenobiotic of interest (XOI) is present/absent, yielding a binary output. Frequentist methods pose a problem when several sources of information [e.g., shape of the chromatographic peak, isotopic distribution, estimated mass-to-charge ratio (m/z), adduct, etc.] need to be combined, requiring the approach to make arbitrary decisions at substep levels of data analysis. We hereby introduce a novel Bayesian probabilistic algorithm for toxicological screening. The method tackles the problem with a different strategy. It is not aimed at reaching a final conclusion regarding the presence of the XOI, but it estimates its probability. The algorithm effectively and efficiently combines all possible pieces of evidence from the chromatogram and calculates the posterior probability of the presence/absence of XOI features. This way, the model can accommodate more information by updating the probability if extra evidence is acquired. The final probabilistic result assists the end user to make a final decision with respect to the presence/absence of the xenobiotic. The Bayesian method was validated and found to perform better (in terms of false positives and false negatives) than the vendor-supplied software package.
作为涉及个体意外死亡案件法医毒理学调查的一部分，通常会对死后样本进行靶向或非靶向的外源性物质筛查。为此，通常采用液相色谱（LC）与高分辨率质谱（MS）联用的方法。对于数据分析，几乎所有常用算法都是基于阈值的（频率论）。这些算法检查某个测量值（例如峰高），以确定是否存在某种感兴趣的外源性物质（XOI），从而产生二元输出。当需要组合多个信息源（例如色谱峰的形状、同位素分布、估计的质荷比（m/z）、加合物等）时，频率论方法会带来问题，这要求该方法在数据分析的子步骤级别做出任意决策。我们在此引入一种用于毒理学筛查的新型贝叶斯概率算法。该方法采用不同的策略解决问题。它的目的不是就XOI的存在得出最终结论，而是估计其概率。该算法有效且高效地组合色谱图中的所有可能证据，并计算XOI特征存在/不存在的后验概率。这样，如果获得额外证据，模型可以通过更新概率来容纳更多信息。最终的概率结果有助于终端用户就外源性物质的存在/不存在做出最终决策。经过验证，贝叶斯方法在误报和漏报方面比供应商提供的软件包表现更好。
