Animal family discrimination from hair using ATR-FTIR and machine learning methods for applications in illegal wildlife trafficking.

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.