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<p>Bloodstain Pattern Analysis (BPA) is a technique used in forensic investigations to recreate violent crime scenes by examining the distribution of bloodstain patterns to interpret the dynamics of the occurrence, including the impact directions, movement of the participants, and the type of weapon that may have been used. Its interpretiveness is, however, severely limited due to underlying limitations, such as the ambiguity of theories of causation patterns, large classification error rates (e.g., 23–30% in controlled studies), distortions created by the surface, environmental contaminations, and a tendency to cognitive errors. This comprehensive survey critically examines these issues, drawing on current meta-analyses and empirical evidence to identify the shortcomings of one-to-many mappings, where a network of mechanisms generates identical patterns, and how these issues can be used to assess judicial reliability. Such influential factors include the biological (e.g. viscosity changes), and physical aspects (e.g. surface tension) of blood, and the surface textures, impact velocities (classified as low: 5 ft/s or less; medium: 5–25 ft/s, high: &gt;100 ft/s), and the fabric properties such as weave density and the rate of absorption, which can change the morphology of the stain by up to 40%. The error rates in fabric classifications are found to be 23.4%, and satellite stains are misidentified at a rate of 59%, which is exacerbated by false contextual transformations. In response to this, the review suggests refined element-based classification systems (e.g., OSAC 2022-S-0030), bias-reduction measures such as Linear Sequential Unmasking Expanded (LSU-E), and technological integrations, including 3D laser scanning and machine-learning-based automated feature extraction. Future research perspectives, such as interlaboratory validation and regional surface data, are suggested to promote the scientific rigour of BPA, especially when using porous textiles, which are dominant in various parts of Asia. This objective analysis highlights the expediency of BPA, though caution is needed to avoid the phenomenon of overreaching evidence.</p>

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Advancements and challenges in bloodstain pattern analysis: Addressing limitations for enhanced forensic reliability

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月27日

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  • Mayur Sudhir Balbudhe,
  • B. Suresh Kumar Shetty,
  • Riya Render,
  • Nayanatara Arun Kumar

摘要

Bloodstain Pattern Analysis (BPA) is a technique used in forensic investigations to recreate violent crime scenes by examining the distribution of bloodstain patterns to interpret the dynamics of the occurrence, including the impact directions, movement of the participants, and the type of weapon that may have been used. Its interpretiveness is, however, severely limited due to underlying limitations, such as the ambiguity of theories of causation patterns, large classification error rates (e.g., 23–30% in controlled studies), distortions created by the surface, environmental contaminations, and a tendency to cognitive errors. This comprehensive survey critically examines these issues, drawing on current meta-analyses and empirical evidence to identify the shortcomings of one-to-many mappings, where a network of mechanisms generates identical patterns, and how these issues can be used to assess judicial reliability. Such influential factors include the biological (e.g. viscosity changes), and physical aspects (e.g. surface tension) of blood, and the surface textures, impact velocities (classified as low: 5 ft/s or less; medium: 5–25 ft/s, high: >100 ft/s), and the fabric properties such as weave density and the rate of absorption, which can change the morphology of the stain by up to 40%. The error rates in fabric classifications are found to be 23.4%, and satellite stains are misidentified at a rate of 59%, which is exacerbated by false contextual transformations. In response to this, the review suggests refined element-based classification systems (e.g., OSAC 2022-S-0030), bias-reduction measures such as Linear Sequential Unmasking Expanded (LSU-E), and technological integrations, including 3D laser scanning and machine-learning-based automated feature extraction. Future research perspectives, such as interlaboratory validation and regional surface data, are suggested to promote the scientific rigour of BPA, especially when using porous textiles, which are dominant in various parts of Asia. This objective analysis highlights the expediency of BPA, though caution is needed to avoid the phenomenon of overreaching evidence.