Animal family discrimination from hair using ATR-FTIR and machine learning methods for applications in illegal wildlife trafficking.
Animal hair
Family discrimination
Machine learning
Python
Spectroscopy
Wildlife forensics
Journal
Die Naturwissenschaften
ISSN: 1432-1904
Titre abrégé: Naturwissenschaften
Pays: Germany
ID NLM: 0400767
Informations de publication
Date de publication:
24 Oct 2024
24 Oct 2024
Historique:
received:
05
08
2024
accepted:
08
10
2024
revised:
05
10
2024
medline:
24
10
2024
pubmed:
24
10
2024
entrez:
24
10
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 39446166
doi: 10.1007/s00114-024-01944-2
pii: 10.1007/s00114-024-01944-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
59Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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