2D Source area prediction based on physical characteristics of a regular, passive blood drip stain. (September 2016)

• Record Type: Journal Article
• Authors: Basu, Nabanita; Bandyopadhyay, Samir Kumar
• Is Part Of: Forensic science international. Volume 266(2016)
• Page Start: 39
• Page End: 53
• Publication Date: 2016-09
• Subjects: Bloodstain patterns -- Crime scene -- Drip stain -- Source dimension -- Prediction -- Supervised learning -- SVM -- K-NN -- Naïve Bayes -- Discriminant analysis; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2016.04.024
• Languages: English
• ISSNs: 0379-0738

Abstract

Highlights: ⿢ Bloodstain pattern unaffected by addition of anticoagulant to blood. ⿢ As the angle of impact increases number of spines also increase. ⿢ As fall height increases, number of spines also increase. ⿢ Blood drop surface area diameter affects the breadth of the stain pattern. ⿢ Possible to predict range of diameter of the source surface area from drip stain. Abstract: Violent criminal acts are often accompanied by dynamic blood shedding events. Bloodstain pattern analysis particularly deals with estimation of the dynamic blood shedding events from the static bloodstain patterns that have been left at the scene. Of all the stain patterns present at a crime scene, drip stain patterns are common stain patterns one would expect to document at a violent crime scene. The paper documents statistically significant correlations between different physical parameters, such as fall height, total number of spines associated with each stain. Statistical significant correlation between the angle of impact and the total number of spines associated with each stain pattern has been established in this work. The paper propounds that the breadth of a regular drip stain is particularly significant in making predictions empirically as also statistically about the surface area from which blood has dripped leading to the formation of a particular drip stain. A data model has been developed using machine learning techniques to predict the range of surface radius from which blood has dripped and lead to the formation of a particular drip stain (Accuracy: 97.53%, Sensitivity = 0.9481, Specificity = 1).