Detection of residues from explosive manipulation by near infrared hyperspectral imaging: a promising forensic tool

A preliminary study of fingerprint aging using near infrared hyperspectral imaging (NIR-HSI)

Fingerprinting is one of the most commonly used techniques to obtain pieces of evidence for identification of individuals. An estimation of how long a trace has been left at a crime scene could represent an important improvement for criminal investigations. There is no reliable analytical method, however, to estimate the age of a fingerprint, since this is an uncontrolled process and changes are affected by factors such as environmental conditions. This study aims to better understand the aging process of fingerprints and identify the relevant variables and limitations of the fingerprint aging process using near infrared hyperspectral imaging (NIR-HSI). For this purpose, aging of the fingerprints of 13 volunteers was evaluated using partial least squares - discriminant analysis (PLS-DA) as a preliminary exploratory approach. Four different modelling approaches were evaluated. The percentage of correctly classified pixels varied from 20.92% to 66.67%. An analysis of the associated spectra found that during the first days of aging the degradation of fat-soluble components, as well as the elimination/absorption of water, seemed to follow non-uniform trends and vary in degradation rate from donor to donor. Better classification tended to occur over longer aging times.

Discrimination of human and animal bloodstains using hyperspectral imaging

Blood is the most encountered type of biological evidence in violent crimes and contains pertinent information to a forensic investigation. The false presumption that blood encountered at a crime scene is human may not be realised until after costly and sample-consuming tests are performed. To address the question of blood origin, the novel application of visible-near infrared hyperspectral imaging (HSI) is used for the detection and discrimination of human and animal bloodstains. The HSI system used is a portable, non-contact, non-destructive method for the determination of blood origin. A support vector machine (SVM) binary classifier was trained for the discrimination of bloodstains of human (n = 20) and five animal species: pig (n = 20), mouse (n = 16), rat (n = 5), rabbit (n = 5), and cow (n = 20). On an independent test set, the SVM model achieved accuracy, precision, sensitivity, and specificity values of 96, 97, 95, and 96%, respectively. Segmented images of bloodstains aged over a period of two months were produced, allowing for the clear visualisation of the discrimination of human and animal bloodstains. The inclusion of such a system in a forensic investigation workflow not only removes ambiguity surrounding blood origin, but can potentially be used in tandem with HSI bloodstain age determination methods for rapid on-scene forensic analysis.

Rapid and On-Scene Chemical Identification of Intact Explosives with Portable Near-Infrared Spectroscopy and Multivariate Data Analysis

There is an ongoing forensic and security need for rapid, on-scene, easy-to-use, non-invasive chemical identification of intact energetic materials at pre-explosion crime scenes. Recent technological advances in instrument miniaturization, wireless transfer and cloud storage of digital data, and multivariate data analysis have created new and very promising options for the use of near-infrared (NIR) spectroscopy in forensic science. This study shows that in addition to drugs of abuse, portable NIR spectroscopy with multivariate data analysis also offers excellent opportunities to identify intact energetic materials and mixtures. NIR is able to characterize a broad range of chemicals of interest in forensic explosive investigations, covering both organic and inorganic compounds. NIR characterization of actual forensic casework samples convincingly shows that this technique can handle the chemical diversity encountered in forensic explosive investigations. The detailed chemical information contained in the 1350-2550 nm NIR reflectance spectrum allows for correct compound identification within a given class of energetic materials, including nitro-aromatics, nitro-amines, nitrate esters, and peroxides. In addition, the detailed characterization of mixtures of energetic materials, such as plastic formulations containing PETN (pentaerythritol tetranitrate) and RDX (trinitro triazinane), is feasible. The results presented illustrate that the NIR spectra of energetic compounds and mixtures are sufficiently selective to prevent false-positive results for a broad range of food-related products, household chemicals, raw materials used for the production of home-made explosives, drugs of abuse, and products that are sometimes used to create hoax improvised explosive devices. However, for frequently encountered pyrotechnic mixtures, such as black powder, flash powder, and smokeless powder, and some basic inorganic raw materials, the application of NIR spectroscopy remains challenging. Another challenge is presented by casework samples of contaminated, aged, and degraded energetic materials or poor-quality HMEs (home-made explosives), for which the spectral signature deviates significantly from the reference spectra, potentially leading to false-negative outcomes.

Hyperspectral imaging in forensic science: an overview of major application areas

Analysis of evidence is a challenge. Crime scene materials are complex, diverse, sometimes of an unknown nature. Forensic science provides the most critical applications for their examination. Chemical tests, analytical methods, and techniques to process the evidence must be carefully selected by the forensic scientist. Ideally, it may be interpreted, analyzed, and judged in the original context of the crime scene. In this sense, hyperspectral imaging (HSI) has been employed as an analytical tool that maintains the integrity of the samples/objects for multiple and sequential analysis and for counter-proof exams. This paper is an overview of forensic science trends for the application of HSI techniques in the last ten years (2011-2021). The examination of documents was the main area of exploration, followed by bloodstain analysis aging process; trace analysis of explosives and gunshot residue. Chemometric tools were also addressed since they are crucial to obtain the most important information from the samples. There are great challenges in applying HSI in forensic science, but there have been clear technological and scientific advances, and a solid foundation has been built for the use of HSI in real-life cases.

Non-destructive detection and classification of textile fibres based on hyperspectral imaging and 1D-CNN

Textile fibre is very common in daily life, and its classification and identification play an important role in textile recycling, archaeology, public security, and other industries. However, traditional identification methods are time-consuming, laborious, and often destructive to the samples. In order to quickly, accurately, and nondestructively classify and recognize textile fibres, this study established a textile fibre classification and recognition method based on hyperspectral imaging (HSI) and a one-dimensional convolutional neural network (1D-CNN) model. Hyperspectral images of 25 kinds of commercial textile fibres were collected and denoised by pixel fusion. Four traditional machine learning classification models, k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and partial least squares-discriminant analysis (PLS-DA), were used to identify the data. The results show that RF has the highest classification accuracy, reaching 91.4%. Then a back propagation neural network (BPNN) model and a one-dimensional convolutional neural network (1D-CNN) model were constructed and compared with the traditional machine learning methods. The results show that the 1D-CNN models have 97.9% and 98.6% accuracy on the training and test sets, respectively. The precision (Pr), sensitivity (Se), specificity (Sp), and F1 score (F1 score) of the models reached 98.7%, 98.6%, 99.9%, and 98.6%, respectively, which were significantly better than the four traditional machine learning models. It seems that 1D-CNN combined with the HSI technique may be a potential method in the detection and classification of textile fibres.

Visible and Near-Infrared hyperspectral imaging (HSI) can reliably quantify CD3 and CD45 positive inflammatory cells in myocarditis: Pilot study on formalin-fixed paraffin-embedded specimens from myocard obtained during autopsy

INTRODUCTION

To implement Hyperspectral Imaging (HSI) as a tool for quantifying inflammatory cells in tissue specimens by the example of myocarditis in a collective of forensic patients.

MATERIAL AND METHODS

44 consecutive patients with suspected myocardial inflammation at autopsy, diagnosed between 2013 and 2018 at the Institute of ForensicMedicine, Medical University of Innsbruck, were selected for this study. Using the IMEC SNAPSCAN camera, visible and near infrared hyperspectral images were collected from slides stained with CD3 and CD45 to assess quantity and spatial distribution of positive cells. Results were compared with visual assessment (VA) and conventional digital image analysis (DIA).

RESULTS

Finally, specimens of 40 patients were evaluated, of whom 36 patients (90%) suffered from myocarditis, two patients (5%) had suspected healing/healed myocarditis, and two did no have myocarditis (5%). The amount of CD3 and CD45 positive cells did not differ significantly between VA, HSI, and DIA (p^VA/HSI/DIA = 0.46 for CD3 and 0.81 for CD45). Coheńs Kappa showed a very high correlation between VA versus HSI, VA versus DIA, and HSI versus DIA for CD3 (Coheńs Kappa = 0.91, 1.00, and 0.91, respectively). For CD45 an almost as high correlation was seen for VA versus HSI and HSI versus DIA (Coheńs Kappa = 0.75 and 0.70) and VA versus DIA (Coheńs Kappa = 0.89).

CONCLUSION

HSI is a reliable and objective method to count inflammatory cells in tissue slides of suspected myocarditis. Implementation of HSI in digital pathology might further expand the possibility of a sophisticated method.

Hyperspectral imaging and artificial intelligence to detect oral malignancy - part 1 - automated tissue classification of oral muscle, fat and mucosa using a light-weight 6-layer deep neural network

Background

Hyperspectral imaging (HSI) is a promising non-contact approach to tissue diagnostics, generating large amounts of raw data for whose processing computer vision (i.e. deep learning) is particularly suitable. Aim of this proof of principle study was the classification of hyperspectral (HS)-reflectance values into the human-oral tissue types fat, muscle and mucosa using deep learning methods. Furthermore, the tissue-specific hyperspectral signatures collected will serve as a representative reference for the future assessment of oral pathological changes in the sense of a HS-library.

Methods

A total of about 316 samples of healthy human-oral fat, muscle and oral mucosa was collected from 174 different patients and imaged using a HS-camera, covering the wavelength range from 500 nm to 1000 nm. HS-raw data were further labelled and processed for tissue classification using a light-weight 6-layer deep neural network (DNN).

Results

The reflectance values differed significantly (p < .001) for fat, muscle and oral mucosa at almost all wavelengths, with the signature of muscle differing the most. The deep neural network distinguished tissue types with an accuracy of > 80% each.

Conclusion

Oral fat, muscle and mucosa can be classified sufficiently and automatically by their specific HS-signature using a deep learning approach. Early detection of premalignant-mucosal-lesions using hyperspectral imaging and deep learning is so far represented rarely in in medical and computer vision research domain but has a high potential and is part of subsequent studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13005-021-00292-0.

Chemometrics in forensic science: approaches and applications

Forensic investigations are often reliant on physical evidence to reconstruct events surrounding a crime. However, there remains a need for more objective approaches to evidential interpretation, along with rigorously validated procedures for handling, storage and analysis. Chemometrics has been recognised as a powerful tool within forensic science for interpretation and optimisation of analytical procedures. However, careful consideration must be given to factors such as sampling, validation and underpinning study design. This tutorial review aims to provide an accessible overview of chemometric methods within the context of forensic science. The review begins with an overview of selected chemometric techniques, followed by a broad review of studies demonstrating the utility of chemometrics across various forensic disciplines. The tutorial review ends with the discussion of the challenges and emerging trends in this rapidly growing field.

Quasi-collinear IR AOTF based on mercurous halide single crystals for spatio-spectral hyperspectral imaging

The paper aims to show the advantages of the infrared-optimised quasi-collinear AOTF (acousto-optic tunable filter) for the spatio-spectral hyperspectral imaging system. The optimisation process is presented based on the selected tetragonal anisotropic materials with exceptional optical and acousto-optical properties in IR (infrared) spectral region. These materials are further compared in terms of their features and suitability for AOTF design. The spectral resolution is considered as the main optimising parameter. Resulting from the analysis, the mercurous chloride (Hg₂Cl₂) single crystal is selected as a representative of the mercurous halide family for the presentation of the quasi-collinear AOTF model operating in LWIR (long-wave infrared) spectral band. The overall parameters of the AOTF model such as spectral resolution, chromatic field of view, acoustic frequency, and operational power requirements are estimated and discussed in results.

Near-infrared hyperspectral imaging for monitoring the thickness distribution of thin poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) layers

The thickness of thin layers of the conductive polymer PEDOT:PSS in the range between about 60 and 300 nm was determined by a near-infrared spectroscopic method using a hyperspectral camera. The reflection spectra of the layers do not contain bands, but consist of a moderate slope of the overall reflectance in the range between 1320 and 1850 nm. Despite the low thickness, the spectra show an extremely strong dependence on the thickness of the layers, which allows their use for quantitative measurements. The prediction of quantitative thickness data from the reflection spectra was based on a chemometric approach using the partial least squares (PLS) algorithm. Calibration was carried out by means of spin-coated layers of PEDOT:PSS, whose thickness was determined by white-light interferometry and stylus profilometry. Finally, this resulted in a calibration model with a root mean square error of prediction (RMSEP) of about 9 nm. After external validation of this model, it was used for quantitative imaging of the thickness distribution in PEDOT:PSS layers. The precision of the predicted values was confirmed by comparison with data from the reference methods. Moreover, it was shown that this approach can be also used for hyperspectral imaging of the thickness of thin printed layers and structures of this conductive polymer on polymer film or paper with excellent thickness resolution. This analytical approach opens new possibilities for in-line process control by large-scale monitoring of thickness and homogeneity of thin layers of conductive polymers.

Visualization of quantitative lipid distribution in mouse liver through near-infrared hyperspectral imaging

Lipid distribution in the liver provides crucial information for diagnosing the severity of fatty liver and fatty liver-associated liver cancer. Therefore, a noninvasive, label-free, and quantitative modality is eagerly anticipated. We report near-infrared hyperspectral imaging for the quantitative visualization of lipid content in mouse liver based on partial least square regression (PLSR) and support vector regression (SVR). Analysis results indicate that SVR with standard normal variate pretreatment outperforms PLSR by achieving better root mean square error (15.3 mg/g) and higher determination coefficient (0.97). The quantitative mapping of lipid content in the mouse liver is realized using SVR.

A dataset for evaluating blood detection in hyperspectral images

The sensitivity of imaging spectroscopy to haemoglobin derivatives makes it a promising tool for detecting blood. However, due to complexity and high dimensionality of hyperspectral images, the development of hyperspectral blood detection algorithms is challenging. To facilitate their development, we present a new hyperspectral blood detection dataset. This dataset, published under an open access license, consists of multiple detection scenarios with varying levels of complexity. It allows to test the performance of Machine Learning methods in relation to different acquisition environments, types of background, age of blood and presence of other blood-like substances. We have explored the dataset with blood detection experiments, for which we have used a hyperspectral target detection algorithm based on the well-known Matched Filter detector. Our results and their discussion highlight the challenges of blood detection in hyperspectral data and form a reference for further works.

Screening of Gunshot Residue in Skin Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Hyperspectral Microscopy

The detection of gunshot residues (GSR) in skin is important in criminal forensic investigations related with firearms. Conventionally, the procedure is based on the detection of metallic or inorganic residues (IGSR). In this work, we propose attenuated total reflectance Fourier transform infrared (ATR FT-IR) hyperspectral microscopy as a complementary and nondestructive technique for detection of organic GSR (OGSR). The spectra were acquired from GSR of three ammunition manufacturers, which were collected from shooter's hands by the tape-lifting method. Before spectroscopic analysis, a Na-Ca bleach solution was added to all GSR samples on the tape for destroying skin debris. Positive detection of OGSR spectra were achieved by ATR FT-IR hyperspectral microscopy. Spectra show characteristic patterns of nitrate ester compounds which agrees with the propellant chemical composition. Characteristic ATR FT-IR spectral patterns of OGSR were measured from visualized GSR particles demonstrating the potential of ATR FT-IR hyperspectral microscopy.

High-Level Synthesis of Multiclass SVM Using Code Refactoring to Classify Brain Cancer from Hyperspectral Images

Currently, high-level synthesis (HLS) methods and tools are a highly relevant area in the strategy of several leading companies in the field of system-on-chips (SoCs) and field programmable gate arrays (FPGAs). HLS facilitates the work of system developers, who benefit from integrated and automated design workflows, considerably reducing the design time. Although many advances have been made in this research field, there are still some uncertainties about the quality and performance of the designs generated with the use of HLS methodologies. In this paper, we propose an optimization of the HLS methodology by code refactoring using Xilinx SDSoCTM (Software-Defined System-On-Chip). Several options were analyzed for each alternative through code refactoring of a multiclass support vector machine (SVM) classifier written in C, using two different Zynq®-7000 SoC devices from Xilinx, the ZC7020 (ZedBoard) and the ZC7045 (ZC706). The classifier was evaluated using a brain cancer database of hyperspectral images. The proposed methodology not only reduces the required resources using less than 20% of the FPGA, but also reduces the power consumption −23% compared to the full implementation. The speedup obtained of 2.86× (ZC7045) is the highest found in the literature for SVM hardware implementations.

Identification of inorganic oxidizing salts in homemade explosives using Fourier transform infrared spectroscopy

Recently, inorganic low explosives, such as pyrotechnic composition, black powder, and ammonium nitrate, are commonly used in improvised explosive devices (IEDs) by the rioter or terrorists since these energetic materials can be obtained easily and legally from civilian markets. Identification of inorganic oxidizing salts in these homemade explosives, including nitrates, chlorates, and perchlorates, is a necessary procedure for forensic investigators to provide criminal evidences. In this article, Fourier transform infrared (FTIR) spectroscopy was used to discriminate NO₃^-, CO₃^2-, ClO₃^-, ClO₄^-, SO₄^2-, and NH₄⁺, whose characteristic absorption bands were explained by vibration modes of the covalent bonds. Then the spectral absorption features of nitrate salts with monovalent or divalent cations were discussed. Furthermore, it was studied whether nitrates or perchlorates can be unequivocally distinguished with the presence of carbonate and sulfate impurities through FTIR technique. Finally, the feasibility of this method was verified through an analytical case of homemade explosives.

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to capture much more information from a certain scene, both within and beyond the visual spectral range (from 400 to 700 nm). This imaging modality is based on the principle that each material provides different responses to light reflection, absorption, and scattering across the electromagnetic spectrum. Due to these properties, it is possible to differentiate and identify the different materials/substances presented in a certain scene by their spectral signature. Over the last two decades, HSI has demonstrated potential to become a powerful tool to study and identify several diseases in the medical field, being a non-contact, non-ionizing, and a label-free imaging modality. In this review, the use of HSI as an imaging tool for the analysis and detection of cancer is presented. The basic concepts related to this technology are detailed. The most relevant, state-of-the-art studies that can be found in the literature using HSI for cancer analysis are presented and summarized, both in-vivo and ex-vivo. Lastly, we discuss the current limitations of this technology in the field of cancer detection, together with some insights into possible future steps in the improvement of this technology.

Interpreting the near infrared region of explosives

The NIR spectra from 1000 to 2500 nm of 18 different explosives, propellant powders and energetic salts were collected and interpreted. NIR spectroscopy is known to provide information about the combination bands and overtones of highly anharmonic vibrations as those occurring in XH bonds (CH, NH and OH). Particularly intense and complex were the bands corresponding to the first combination region (2500-1900 nm) and first overtone stretching mode (2ν) of CH and NH bonds (1750-1450 nm). Inorganic oxidizing salts including sodium/potassium nitrate, sodium/potassium chlorate, and sodium/potassium perchlorate displayed low intense or no NIR bands.

Study of the adhesion of explosive residues to the finger and transfer to clothing and luggage

It is important to understand the extent of transfer of explosive particles to different surfaces in order to better evaluate potential cross-contamination by explosives in crowded security controls such as those at airports. This work investigated the transfer of nine explosive residues (ANFO, dynamite, black powder, TNT, HMTD, PETN, NH₄NO₃, KNO₃, NaClO₃) through fingerprints from one surface to another. First, the extent of adhesion of explosive residues from different surfaces to the bare finger, nitrile and latex gloves was studied. Then, the transfer of explosive residues from one surface to another through fingerprints was investigated. Cotton fabric (hereinafter referred to as cotton) as clothing material and polycarbonate plastic (hereinafter referred to as polycarbonate) as luggage material were chosen for the experiments. These surfaces containing explosive particles were imaged using a reflex camera before and after the particles were transferred. Afterwards the images were processed in MATLAB where pixels corresponding to explosive residues were quantified. Results demonstrated that transfer of explosive residues frequently occurred with certain differences among materials. Generally, the amount of explosive particles adhered to the finger decreased in the following order: skin>latex>nitrile, while the transfer of particles from the finger to another surface was the opposite. The adhesion of explosive residues from polycarbonate to the finger was found to be better compared to cotton, while the amount of particles transferred to cotton was higher.

Non-invasive sensing for food reassurance

The main food quality traits of interest using non-invasive sensing techniques are sensory characteristics, chemical composition, physicochemical properties, health-protecting properties, nutritional characteristics and safety. A wide range of non-invasive sensing techniques, from optical, acoustical, electrical, to nuclear magnetic, X-ray, biosensor, microwave and terahertz, are organized according to physical principle.

Advances in explosives analysis--part II: photon and neutron methods

The number and capability of explosives detection and analysis methods have increased dramatically since publication of the Analytical and Bioanalytical Chemistry special issue devoted to Explosives Analysis [Moore DS, Goodpaster JV, Anal Bioanal Chem 395:245-246, 2009]. Here we review and critically evaluate the latest (the past five years) important advances in explosives detection, with details of the improvements over previous methods, and suggest possible avenues towards further advances in, e.g., stand-off distance, detection limit, selectivity, and penetration through camouflage or packaging. The review consists of two parts. Part I discussed methods based on animals, chemicals (including colorimetry, molecularly imprinted polymers, electrochemistry, and immunochemistry), ions (both ion-mobility spectrometry and mass spectrometry), and mechanical devices. This part, Part II, will review methods based on photons, from very energetic photons including X-rays and gamma rays down to the terahertz range, and neutrons.

Hyperspectral image analysis. A tutorial

This tutorial aims at providing guidelines and practical tools to assist with the analysis of hyperspectral images. Topics like hyperspectral image acquisition, image pre-processing, multivariate exploratory analysis, hyperspectral image resolution, classification and final digital image processing will be exposed, and some guidelines given and discussed. Due to the broad character of current applications and the vast number of multivariate methods available, this paper has focused on an industrial chemical framework to explain, in a step-wise manner, how to develop a classification methodology to differentiate between several types of plastics by using Near infrared hyperspectral imaging and Partial Least Squares - Discriminant Analysis. Thus, the reader is guided through every single step and oriented in order to adapt those strategies to the user's case.

Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection

Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the ‘curse of dimensionality’ have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers –based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations.