- Titel
- Discrimination of human and animal bloodstains using hyperspectral imaging
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-1042797
- Quellenangabe
- Forensic Science, Medicine and Pathology
Erscheinungsjahr: 2024
Jahrgang: 20
Heft: 2
Seiten: 490-499
E-ISSN: 1556-2891 - Erstveröffentlichung
- 2024
- Abstract (EN)
- Blood is the most encountered type of biological evidence in violent crimes and contains pertinent information to a forensic investigation. The false presumption that blood encountered at a crime scene is human may not be realised until after costly and sample-consuming tests are performed. To address the question of blood origin, the novel application of visible-near infrared hyperspectral imaging (HSI) is used for the detection and discrimination of human and animal bloodstains. The HSI system used is a portable, non-contact, non-destructive method for the determination of blood origin. A support vector machine (SVM) binary classifier was trained for the discrimination of bloodstains of human (n = 20) and five animal species: pig (n = 20), mouse (n = 16), rat (n = 5), rabbit (n = 5), and cow (n = 20). On an independent test set, the SVM model achieved accuracy, precision, sensitivity, and specificity values of 96, 97, 95, and 96%, respectively. Segmented images of bloodstains aged over a period of two months were produced, allowing for the clear visualisation of the discrimination of human and animal bloodstains. The inclusion of such a system in a forensic investigation workflow not only removes ambiguity surrounding blood origin, but can potentially be used in tandem with HSI bloodstain age determination methods for rapid on-scene forensic analysis.
- Andere Ausgabe
- Erstveröffentlichung
DOI: 10.1007/s12024-023-00689-0 - Freie Schlagwörter (EN)
- Animal blood, Hyperspectral imaging (HSI), Support vector machine (SVM), Neighbourhood component feature selection (NCFS), Forensics
- Klassifikation (DDC)
- 610
- Verlag
- Springer US, New York
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:15-qucosa2-1042797
- Veröffentlichungsdatum Qucosa
- 30.04.2026
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0