Institute of Forensic Science and Criminology, Panjab University, Chandigarh, 160014, India.
Naturwissenschaften. 2024 Oct 24;111(6):59. doi: 10.1007/s00114-024-01944-2.
Wildlife forensics plays a pivotal role in the combating illegal trafficking, supporting biodiversity conservation, and aiding in the identification of animals in wildlife. Animal hair, often found in trafficking crimes, serves as vital biological evidence that can provide significant information for animal identification. This study proposes a novel method integrating machine learning classifiers with Fourier transform infrared (FTIR) spectroscopy in attenuated total reflectance (ATR) mode to enhance the effectiveness of animal identification in wildlife forensic casework. Additionally, compound microscopy has also been utilized as a preliminary tool to perform morphological analysis of hair samples from four animal families, including Bovidae, Cervidae, Elephantidae, and Felidae. Further, chemical profiling through spectral data revealed significant overlapping peaks between family Bovidae and Cervidae. The classification experiment provides the random forest (RF) classifier as the most effective for family discrimination model. This research offers valuable insights for wildlife forensics by improving the identification accuracy of unknown hair samples, thus enhancing the overall effectiveness in forensic investigations.
野生动物法医学在打击非法贸易、支持生物多样性保护以及协助识别野生动物中的动物方面发挥着关键作用。在走私犯罪中经常发现的动物毛发是重要的生物证据，可以为动物识别提供重要信息。本研究提出了一种将机器学习分类器与傅里叶变换红外（FTIR）光谱法结合在衰减全反射（ATR）模式下的新方法，以提高野生动物法医学案件中动物识别的有效性。此外，复合显微镜也被用作初步工具，对来自牛科、鹿科、象科和猫科等四个动物科的毛发样本进行形态分析。此外，通过光谱数据进行的化学特征分析显示牛科和鹿科之间存在显著的重叠峰。分类实验表明，随机森林（RF）分类器是最有效的家族鉴别模型。本研究通过提高未知毛发样本的识别准确性，为野生动物法医学提供了有价值的见解，从而提高了法医学调查的整体效果。
