The use of LA-ICP-MS databases to calculate analysis of glass evidence
The use of LA-ICP-MS databases to calculate analysis of glass evidence
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DOI：
10.1016/j.talanta.2018.02.027
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发表时间：
2018-08-15
期刊：
影响因子：
6.1
通讯作者：
Almirall, Jose
中科院分区：
文献类型：
--
作者：
Corzo, Ruthmara;Hoffman, Tricia;Almirall, Jose
激光烧蚀-电感耦合等离子体质谱（LA-ICP-MS）已被证明是区分不同来源的玻璃和来自同一来源的玻璃的联合的优秀技术。通常，匹配标准用于比较已知样品与质疑样品的元素分布，并且如果玻璃样品被确定为“匹配”，则可以随后使用口头量表来报告法医从业者的结论。这种方法有几个缺点：一个固定的匹配标准遭受“跌落悬崖效应”，一个元素配置文件的稀有性没有考虑在内，使用口头尺度来分配证据的权重可能被认为是主观的，并且可以由审查员改变。另一种方法包括使用连续似然比，该方法提供了支持任何假设的证据价值的定量测量，并通过使用玻璃数据库解释了元素分布的稀有性。在本研究中，使用两个玻璃数据库来评估似然比的性能;第一个数据库包括420个汽车挡风玻璃样品，而第二个数据库包括385个玻璃样品。使用多变量核模型计算似然比。然而，这个模型导致了不合理的大（或小）似然比。因此，为了将似然比限制在合理的值，使用池相邻违规者（PAV）算法的校准步骤是必要的。校准后的似然比显示误导性证据的发生率< 1.5%（当物体来自同一来源时，LR < 1），< 1.0%（当物体来自不同来源时，LR> 1），这比以前报告的类似ASTM错误纳入和错误排除率有所改善。此外，似然比限制了误导性证据的数量，仅为不正确的假设提供弱到中等的支持。最后，当物体来自不同来源时，大多数发现呈现LR > 1的对被解释为玻璃源制造商的相似性。
\ Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) has been shown to be an excellent technique for the discrimination of glass originating from different sources and for the association of glass originating from the same source. Typically, a match criterion is used to compare the elemental profile of the known sample to a questioned sample and if the glass samples are determined to "match" this may be followed by the use of a verbal scale to report the forensic practitioner's conclusion. This approach has several disadvantages: a fixed match criterion suffers from the "fall-off-the-cliff effect," the rarity of an elemental profile is not taken into account, and the use of a verbal scale to assign a weight of evidence may be considered as subjective and can vary by examiner. An alternative approach includes the use of a continuous likelihood ratio that provides a quantitative measure of the value of the evidence in support of any hypothesis and accounts for the rarity of an elemental profile through the use of a glass database. In the present study, two glass databases were used to evaluate the performance of the likelihood ratio; the first database includes 420 automotive windshield samples, while the second database includes 385 glass samples from casework. The multivariate kernel model was used for the calculation of the likelihood ratio. However, this model led to unreasonably large (or small) likelihood ratios. Thus, a calibration step, using the Pool Adjacent Violators (PAV) algorithm, was necessary in order to limit the likelihood ratio to reasonable values. The calibrated likelihood ratio presented rates of misleading evidence of < 1.5% (for LRs < 1 when objects came from the same source), and of < 1.0% (for LRs > 1 when objects came from different sources), which improved over the analogous ASTM false inclusion and false exclusion rates previously reported. In addition, the likelihood ratio limited the magnitude of the misleading evidence, providing only weak to moderate support for the incorrect hypothesis. Finally, most of the pairs found to present LR > 1 when objects originated from different sources were explained by similarity of manufacturer of the glass source.