Ecole des Sciences Criminelles/School of Criminal Justice, Faculty of Law, Criminal Justice, and Public Administration, University of Lausanne, Dorigny, 1015 Lausanne, Switzerland.
Unit of Toxicology, CURML, Vulliette 04, 1000 Lausanne 25, Switzerland.
Forensic Sci Int. 2023 May;346:111645. doi: 10.1016/j.forsciint.2023.111645. Epub 2023 Mar 16.
Fingermark patterns are one of the oldest means of biometric identification. During this last decade, the molecules that constitute the fingermark residue have gained interest among the forensic research community to gain additional intelligence regarding its donor profile including its gender, age, lifestyle or even its pathological state. In this work, the molecular composition of fingermarks have been studied to monitor the variability between donors and to explore its capacity to differentiate individuals using supervised multi-class classification models. Over one year, fingermarks from thirteen donors have been analysed by Matrix-Assisted Laser Desorption/Ionisation Mass Spectrometry Imaging (n = 716) and mined by different machine learning approaches. We demonstrate the potential of the fingermark chemical composition to help differentiating individuals with an accuracy between 80% and 96% depending on the period of sample collection for each donor and size of the pool of donors. It would be premature at this stage to transpose the results of this research to real cases, however the conclusions of this study can provide a better understanding of the variations of the chemical composition of the fingermark residue in between individuals over long periods and help clarifying the notion of donorship.
指纹模式是最古老的生物识别手段之一。在过去的十年中，构成指纹残留物的分子引起了法医研究界的兴趣，以获取关于其供体特征的更多信息，包括其性别、年龄、生活方式甚至病理状态。在这项工作中，研究了指纹的分子组成，以监测供体之间的可变性，并探索使用监督多类分类模型对个体进行区分的能力。在一年多的时间里，通过基质辅助激光解吸/电离质谱成像（n=716）分析了来自 13 名供体的指纹，并通过不同的机器学习方法进行了挖掘。我们展示了指纹化学成分的潜力，可以帮助区分个体，准确率在 80%到 96%之间，具体取决于每个供体的样本采集时间和供体池的大小。现阶段将这项研究的结果转化为实际案例还为时过早，然而，这项研究的结论可以帮助更好地理解个体之间长时间内指纹残留物化学成分的变化，并帮助澄清供体的概念。
