Forensic analysis of beverage stains using hyperspectral imaging
Abstract Documentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the com...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2021-03-01
|
Series: | Scientific Reports |
Online Access: | https://doi.org/10.1038/s41598-021-85737-x |
id |
doaj-a4b30b89db2941dfac83c06ecb4742ac |
---|---|
record_format |
Article |
spelling |
doaj-a4b30b89db2941dfac83c06ecb4742ac2021-03-28T11:30:47ZengNature Publishing GroupScientific Reports2045-23222021-03-0111111310.1038/s41598-021-85737-xForensic analysis of beverage stains using hyperspectral imagingBinu Melit Devassy0Sony George1Department of Computer Science, Norwegian University of Science and Technology (NTNU)Department of Computer Science, Norwegian University of Science and Technology (NTNU)Abstract Documentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains.https://doi.org/10.1038/s41598-021-85737-x |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Binu Melit Devassy Sony George |
spellingShingle |
Binu Melit Devassy Sony George Forensic analysis of beverage stains using hyperspectral imaging Scientific Reports |
author_facet |
Binu Melit Devassy Sony George |
author_sort |
Binu Melit Devassy |
title |
Forensic analysis of beverage stains using hyperspectral imaging |
title_short |
Forensic analysis of beverage stains using hyperspectral imaging |
title_full |
Forensic analysis of beverage stains using hyperspectral imaging |
title_fullStr |
Forensic analysis of beverage stains using hyperspectral imaging |
title_full_unstemmed |
Forensic analysis of beverage stains using hyperspectral imaging |
title_sort |
forensic analysis of beverage stains using hyperspectral imaging |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-03-01 |
description |
Abstract Documentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains. |
url |
https://doi.org/10.1038/s41598-021-85737-x |
work_keys_str_mv |
AT binumelitdevassy forensicanalysisofbeveragestainsusinghyperspectralimaging AT sonygeorge forensicanalysisofbeveragestainsusinghyperspectralimaging |
_version_ |
1724199884017369088 |