Saville P A, Hainsworth S V, Rutty G N
Forensic Pathology Unit, University of Leicester, Robert Kilpatrick Building, Leicester Royal Infirmary, Leicester, LE2 7LX, UK.
Int J Legal Med. 2007 Sep;121(5):349-57. doi: 10.1007/s00414-006-0120-z. Epub 2006 Sep 26.
Witness marks produced on bone by the use of saws have traditionally been examined using stereomicroscopy. The marks are typically found on the kerf wall or floor and give important information about the implement that made them. This paper describes a new approach to the analysis of witness marks left on kerf walls and floors from crimes involving dismemberment. Previously, two types of marks have been identified: deep furrows formed during the pull stroke and fine striations formed on the push stroke. These types of striation allow the class of saw to be identified, but not an individual saw. With the advent of environmental scanning electron microscopy (ESEM), insulating materials can now be examined without the need for conductive coatings to be applied. This allows materials to be examined at higher magnifications than those available with stereomicroscopy. Here we report on a new, third type of striation that is visible at higher magnifications on ESEM images. These striations are formed from the imperfections on the cutting teeth of saws and give real possibilities of uniquely identifying whether or not a particular saw was used to cause the mark. In blind trials conducted on sawing of nylon 6.6, different individual saws could be successfully identified even if different people used the saw. We discuss ways in which these results can be extended to bone and how this may assist in the investigation of the act of dismemberment.
传统上,使用立体显微镜检查锯在骨头上留下的痕迹。这些痕迹通常出现在锯口壁或底部,能提供有关制造这些痕迹的工具的重要信息。本文描述了一种分析涉及肢解犯罪的锯口壁和底部留下的痕迹的新方法。此前,已识别出两种类型的痕迹:拉动行程中形成的深沟和推动行程中形成的细纹。这些类型的条纹能识别锯的类别,但无法识别单个锯。随着环境扫描电子显微镜(ESEM)的出现,现在可以在不施加导电涂层的情况下检查绝缘材料。这使得材料能够在比立体显微镜更高的放大倍数下进行检查。在此,我们报告一种新的、第三种类型的条纹,在ESEM图像上更高放大倍数下可见。这些条纹由锯的切割齿上的瑕疵形成,真正有可能唯一确定是否使用了特定的锯来造成该痕迹。在对尼龙6.6进行锯切的盲测中,即使不同的人使用锯,也能成功识别不同的单个锯。我们讨论了如何将这些结果扩展到骨头,以及这可能如何有助于肢解行为的调查。