Abstract The comparative analysis of chromatographic profiles of materials is the subject of interest in many scientific fields, including forensic science. Plastic microtraces collected during hit-and-run accidents and examined with pyrolysis gas chromatography mass spectrometry (Py-GC–MS), may serve as an example. The aim of comparing the recovered and control samples is to help reconstruct the event by commenting on their common, or not, sources. The objective is to report the evidential value of data in the context of two competing hypotheses: H1 – both samples share common origins (e.g. car) and H2 – they do not share common origins. The likelihood ratio approach (LR) addresses this idea as an acknowledged method within the forensic community. However, conventional feature-based LR models (using e.g. signal intensities of the chromatographically separated compounds) suffer from the curse of multidimensionality. Their considerable complexity can be reduced in the score-based LR models. In this concept the evidence expressed by the score, computed as a distance between the recovered and control samples characteristics, is evaluated using LR. A score solely based on a distance or a measure of similarity, without taking into account typicality, may not reflect the differences between similar samples clearly in a highly multidimensional space. Here we show that boosting the between-samples variance (B) whilst minimising the within-samples variance (W) helps distinguish between samples and improves the score-based LR models performance. Instead of computing the distances in the feature space, the authors use the space defined by ANOVA simultaneous component analysis, regularised MANOVA and ANOVA target projection that find directions with the magnified differences between B and W. The concept was successfully illustrated for 22 plastic containers and automotive samples, examined using Py-GC–MS. The research shows that this so-called hybrid approach combining chemometric tools and score-based LR framework yields a performing solution for the comparison problem for Py-GC–MS chromatograms.

中文翻译：

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使用基于分数的似然比对热图进行法医比较

摘要 材料色谱图的比较分析是许多科学领域（包括法医科学）感兴趣的主题。在肇事逃逸事故中收集并用热解气相色谱质谱 (Py-GC-MS) 检测的塑料微量痕量可以作为一个例子。比较恢复样本和对照样本的目的是通过评论它们的共同来源或不共同来源来帮助重建事件。目标是在两个相互竞争的假设的背景下报告数据的证据价值：H1——两个样本具有共同的来源（例如汽车）和 H2——它们不具有共同的来源。似然比方法 (LR) 将此想法作为法医界公认的方法加以解决。然而，传统的基于特征的 LR 模型（使用例如 色谱分离化合物的信号强度）受到多维灾难的影响。在基于分数的 LR 模型中可以降低它们相当大的复杂性。在这个概念中，分数表示的证据，计算为恢复和控制样本特征之间的距离，使用 LR 进行评估。仅基于距离或相似性度量的分数，而不考虑典型性，可能无法在高度多维空间中清楚地反映相似样本之间的差异。在这里，我们展示了提高样本间方差 (B) 的同时最小化样本内方差 (W) 有助于区分样本并提高基于分数的 LR 模型性能。而不是计算特征空间中的距离，作者使用 ANOVA 同步分量分析定义的空间、正则化 MANOVA 和 ANOVA 目标投影，通过 B 和 W 之间的放大差异找到方向。该概念已成功用于 22 个塑料容器和汽车样品，使用 Py-GC-MS 检查. 研究表明，这种所谓的混合方法结合了化学计量工具和基于分数的 LR 框架，为 Py-GC-MS 色谱图的比较问题提供了一种有效的解决方案。