LA-ICP-MS is a powerful technique for obtaining a forensic elemental profile of polymer evidence materials. However, the lack of homogeneous polymer reference standards hampers database creation and reliably matching a sample to a specific source. Therefore, the current study aims to evaluate the strength of evidence of forensic polymer comparisons by applying a matrix-matched reference standard with known concentrations for elements of interest. Four datasets of tapes, electrical wires, tubing, and jerrycans were compiled using LA-ICP-MS. It was found that quantification with the new PVC standard or by simply using the response of one element as internal standard significantly reduced the between-run variation. For each class of polymeric materials, characteristic elements could be identified with PCA and LDA. To facilitate classification, elemental concentrations were found to be typical for specific colors and types of polymeric materials. For forensic comparison, a score-based Bayesian likelihood ratio model and the -test overlap method performed better than the feature-based model and 4-sigma criterion, in terms of rates of misleading evidence. Normalization to C and quantification with the PVC standard with and without prior normalization to C slightly reduced rates of misleading evidence. The -test method showed an overall average false inclusion rate of only 0.45% and a false exclusion rate of 2.4%. Maximum calibrated likelihood ratios of 0.014 to 1778 were found for the tape dataset. In conclusion, this study demonstrates that with the use of proper standards, quantitative elemental profiling with LA-ICP-MS is a promising tool for forensic classification and comparison of polymers.

中文翻译：

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使用 LA-ICP-MS 评估聚合物元素分析的证据强度

LA-ICP-MS 是一种用于获取聚合物证据材料法医元素概况的强大技术。然而，缺乏均质聚合物参考标准阻碍了数据库的创建以及将样品与特定来源可靠地匹配。因此，当前的研究旨在通过应用具有已知感兴趣元素浓度的基质匹配参考标准来评估法医聚合物比较的证据强度。使用 LA-ICP-MS 编译了磁带、电线、管道和塑料桶的四个数据集。结果发现，使用新 PVC 标准进行定量或仅使用一种元素的响应作为内标可显着减少运行间变异。对于每一类聚合物材料，可以使用 PCA 和 LDA 来识别特征元素。为了便于分类，我们发现特定颜色和类型的聚合物材料的典型元素浓度。对于取证比较，就误导性证据率而言，基于分数的贝叶斯似然比模型和 测试重叠方法的表现优于基于特征的模型和 4-sigma 标准。对 C 进行归一化并使用 PVC 标准进行量化，无论是否事先对 C 进行归一化，都会略微降低误导性证据的发生率。检验方法显示总体平均误纳入率仅为0.45%，误排除率为2.4%。磁带数据集的最大校准似然比为 0.014 到 1778。总之，本研究表明，通过使用适当的标准，使用 LA-ICP-MS 进行定量元素分析是一种很有前途的聚合物法医分类和比较工具。