Identification of invisible biological traces in forensic evidences by hyperspectral NIR imaging combined with chemometrics

Malegori, Cristina; Alladio, Eugenio; Oliveri, Paolo; Manis, Cristina; Vincenti, Marco; Garofano, Paolo; Barni, Filippo; Berti, Andrea

doi:10.1016/j.talanta.2020.120911

Cited by 34 publications

(18 citation statements)

References 16 publications

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“…When comparing a Crime-lite ® ML2 ALS instrument (Foster and Freeman Ltd., Evesham, UK) and HIS-NIR imaging using an SWIR3 hyperspectral camera working in the 1000-2500 nm spectral range (Specim Ltd., Oulu, Finland) [66], the resultant images obtained from the HIS-NIR imaging were shown to be superior at revealing the location of body fluid staining in known reference samples, simulated casework samples and on unknown staining on forensic exhibits. Using blood samples and different substrates, spectra were generated [67] that were used to train different classifiers to detect aged bloodstains and most recently a "blood detection dataset" was produced [68] specifically to assist in the challenging task of algorithm development for this new technology.…”

Section: Alternative Light Sourcesmentioning

confidence: 99%

On the Identification of Body Fluids and Tissues: A Crucial Link in the Investigation and Solution of Crime

Sijen

Harbison

2021

Genes

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Body fluid and body tissue identification are important in forensic science as they can provide key evidence in a criminal investigation and may assist the court in reaching conclusions. Establishing a link between identifying the fluid or tissue and the DNA profile adds further weight to this evidence. Many forensic laboratories retain techniques for the identification of biological fluids that have been widely used for some time. More recently, many different biomarkers and technologies have been proposed for identification of body fluids and tissues of forensic relevance some of which are now used in forensic casework. Here, we summarize the role of body fluid/ tissue identification in the evaluation of forensic evidence, describe how such evidence is detected at the crime scene and in the laboratory, elaborate different technologies available to do this, and reflect real life experiences. We explain how, by including this information, crucial links can be made to aid in the investigation and solution of crime.

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Section: Alternative Light Sourcesmentioning

confidence: 99%

On the Identification of Body Fluids and Tissues: A Crucial Link in the Investigation and Solution of Crime

Sijen

Harbison

2021

Genes

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“…A crowning achievement of this strategy is the entry of the Team in the forensic field; this was possible thanks to a collaboration with the University of Torino, the Ra.C.I.S., Italian authority for scientific investigation, and the Katowice University (Poland). 9…”

Section: Research Linesmentioning

confidence: 99%

“…S., Italian authority for scientific investigation, and the Katowice University (Poland). 9 Among the confirmed research lines, the development of innovative chemometric strategies is continuing to gain attention, in particular for the implementation of dedicated software aimed at the resolution of specific issues, for both research and industrial applications. To be noted is the collaboration between the research Group and the Italian company Medi Nova, world leader in the production of semen extenders for swine artificial insemination.…”

Section: Research Linesmentioning

confidence: 99%

Analytical Chemistry and Chemometrics Group, Department of Pharmacy, University of Genova: An update

Malegori

Mustorgi

2020

NIR news

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This paper has the purpose of updating the NIR scientific community about the activities that the Group of Analytical Chemistry and Chemometrics of the University of Genova (Italy) is carrying out in recent years. In more detail, the research lines are presented together with the laboratory equipment available. Moreover, an in-depth focus of the teaching activities is given, taking the occasion for presenting the last news regarding software development. The role of the Group in the organisation of schools and conferences in the context of the Italian Society of NIR Spectroscopy (SISNIR) is also detailed.

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“…As an integration of spectroscopy and digital imaging techniques, spectral imaging has emerged as a versatile tool for many applications including remote sensing, 1,2 food sciences, 3,4 pharmaceutical research, 5,6 forensic sciences, 7,8 cultural heritage, 9 agriculture and forestry. 10,11 A spectral image is a three-dimensional (3-D) data array with two spatial dimensions (of x rows and y columns) and one spectral dimension (of l wavelengths).…”

Section: Introductionmentioning

confidence: 99%

Deep learning classifiers for near infrared spectral imaging: a tutorial

Riccioli

Herrero-Langreo

et al. 2020

J. Spectral Imaging

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Deep learning (DL) has recently achieved considerable successes in a wide range of applications, such as speech recognition, machine translation and visual recognition. This tutorial provides guidelines and useful strategies to apply DL techniques to address pixel-wise classification of spectral images. A one-dimensional convolutional neural network (1-D CNN) is used to extract features from the spectral domain, which are subsequently used for classification. In contrast to conventional classification methods for spectral images that examine primarily the spectral context, a three-dimensional (3-D) CNN is applied to simultaneously extract spatial and spectral features to enhance classificationaccuracy. This tutorial paper explains, in a stepwise manner, how to develop 1-D CNN and 3-D CNN models to discriminate spectral imaging data in a food authenticity context. The example image data provided consists of three varieties of puffed cereals imaged in the NIR range (943–1643 nm). The tutorial is presented in the MATLAB environment and scripts and dataset used are provided. Starting from spectral image pre-processing (background removal and spectral pre-treatment), the typical steps encountered in development of CNN models are presented. The example dataset provided demonstrates that deep learning approaches can increase classification accuracy compared to conventional approaches, increasing the accuracy of the model tested on an independent image from 92.33 % using partial least squares-discriminant analysis to 99.4 % using 3-CNN model at pixel level. The paper concludes with a discussion on the challenges and suggestions in the application of DL techniques for spectral image classification.

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Identification of invisible biological traces in forensic evidences by hyperspectral NIR imaging combined with chemometrics

Cited by 34 publications

References 16 publications

On the Identification of Body Fluids and Tissues: A Crucial Link in the Investigation and Solution of Crime

On the Identification of Body Fluids and Tissues: A Crucial Link in the Investigation and Solution of Crime

Analytical Chemistry and Chemometrics Group, Department of Pharmacy, University of Genova: An update

Deep learning classifiers for near infrared spectral imaging: a tutorial

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