Literature DB >> 27162017

The non-contact detection and identification of blood stained fingerprints using visible wavelength hyperspectral imaging: Part II effectiveness on a range of substrates.

Samuel Cadd1, Bo Li2, Peter Beveridge3, William T O'Hare4, Andrew Campbell5, Meez Islam6.   

Abstract

Biological samples, such as blood, are regularly encountered at violent crime scenes and successful identification is critical for criminal investigations. Blood is one of the most commonly encountered fingerprint contaminants and current identification methods involve presumptive tests or wet chemical enhancement. These are destructive however; can affect subsequent DNA sampling; and do not confirm the presence of blood, meaning they are susceptible to false positives. A novel application of visible wavelength reflectance hyperspectral imaging (HSI) has been used for the non-contact, non-destructive detection and identification of blood stained fingerprints across a range of coloured substrates of varying porosities. The identification of blood was based on the Soret γ band absorption of haemoglobin between 400 nm and 500 nm. Ridge detail was successfully visualised to the third depletion across light coloured substrates and the stain detected to the tenth depletion on both porous and non-porous substrates. A higher resolution setup for blood stained fingerprints on black tiles, detected ridge detail to the third depletion and the stain to the tenth depletion, demonstrating considerable advancements from previous work. Diluted blood stains at 1500 and 1000 fold dilutions for wet and dry stains respectively were also detected on pig skin as a replica for human skin.
Copyright © 2016 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Blood detection; Fingerprints; Forensic Science; Hyperspectral Imaging

Mesh:

Substances:

Year:  2016        PMID: 27162017     DOI: 10.1016/j.scijus.2016.01.005

Source DB:  PubMed          Journal:  Sci Justice        ISSN: 1355-0306            Impact factor:   2.124


  1 in total

1.  Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks.

Authors:  Kamil Książek; Michał Romaszewski; Przemysław Głomb; Bartosz Grabowski; Michał Cholewa
Journal:  Sensors (Basel)       Date:  2020-11-21       Impact factor: 3.576

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.