Enhancing forensic blood detection using hyperspectral imaging and advanced preprocessing techniques.

Autor: Al-Alimi D; Department of Information Technology, Gulf Colleges, Hafr Al-Batin, 2600, Saudi Arabia; Faculty of Engineering, Sana'a University, Sana'a, 12544, Yemen. Electronic address: dma@gulf.edu.sa., Al-Qaness MAA; College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China; Zhejiang Institute of Optoelectronics, Jinhua, 321004, China; College of Engineering and Information Technology, Emirates International University, Sana'a, 16881, Yemen. Electronic address: alqaness@zjnu.edu.cn.
Jazyk: angličtina
Zdroj: Talanta [Talanta] 2025 Feb 01; Vol. 283, pp. 127097. Date of Electronic Publication: 2024 Oct 22.
DOI: 10.1016/j.talanta.2024.127097
Abstrakt: Bloodstains are pivotal in forensic investigations as they can provide crucial DNA information about individuals involved in a crime. Traditional methods for bloodstain detection, including chemical tests and forensic lights, have limitations such as non-specificity to human blood and susceptibility to false positives. In forensic blood detection, molecular spectroscopy is crucial for identifying the unique spectral fingerprints of blood, which arise from its molecular composition. Hyperspectral imaging (HSI) leverages this principle by capturing a wide spectrum of light for each pixel in an image, allowing for the detailed analysis of various substances. HSI has emerged as a promising alternative, offering non-contact, rapid, and cost-effective detection of bloodstains by analyzing the visible and near-infrared electromagnetic spectrum. This study explores the application of HSI for blood detection, addressing challenges such as spectral mixing, time-related changes in bloodstain spectra, and data complexity. The study introduces a novel framework to optimize HSI data, enhancing the accuracy and efficiency of bloodstain classification, called the Fast Extraction (FE) framework. It includes two stages. The main method in the first one is the Enhancing Transformation Reduction (ETR) method to reduce the dimension and complexity of the HSI. The second stage contains a compatible classification model to enhance feature extraction and classification. Our approach is validated using the HyperBlood datasets and many evaluation methods, demonstrating superior performance compared to state-of-the-art deep learning models. It provides high accuracy (97%-100 %) for all HSIs, overcoming various difficulties and blood collection times.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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