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Discrimination of human and animal bloodstains using hyperspectral imaging.
Cooney, Gary Sean; Köhler, Hannes; Chalopin, Claire; Babian, Carsten.
Affiliation
  • Cooney GS; Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany.
  • Köhler H; Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany.
  • Chalopin C; Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany.
  • Babian C; Institute for Legal Medicine, Leipzig University, Leipzig, Germany. carsten.babian@medizin.uni-leipzig.de.
Article in En | MEDLINE | ID: mdl-37721660
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Forensic Sci Med Pathol Journal subject: JURISPRUDENCIA / MEDICINA / PATOLOGIA Year: 2023 Document type: Article Affiliation country: Germany Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Forensic Sci Med Pathol Journal subject: JURISPRUDENCIA / MEDICINA / PATOLOGIA Year: 2023 Document type: Article Affiliation country: Germany Country of publication: United States