Multivariate Analysis Techniques in the Forensics Investigation of the Postblast Residues by Means of Fourier Transform-Infrared Spectroscopy

Classification of the Residues after High and Low Order Explosions Using Machine Learning Techniques on Fourier Transform Infrared (FTIR) Spectra

Forensic science is a field that requires precise and reliable methods for the detection and analysis of evidence. One such method is Fourier Transform Infrared (FTIR) spectroscopy, which provides high sensitivity and selectivity in the detection of samples. In this study, the use of FTIR spectroscopy and statistical multivariate analysis to identify high explosive (HE) materials (C-4, TNT, and PETN) in the residues after high- and low-order explosions is demonstrated. Additionally, a detailed description of the data pre-treatment process and the use of various machine learning classification techniques to achieve successful identification is also provided. The best results were obtained with the hybrid LDA-PCA technique, which was implemented using the R environment, a code-driven open-source platform that promotes reproducibility and transparency.

Forensic Discrimination of Drug Powder Based on Drug Mixing Condition Determined Using Micro Fourier Transform Infrared Spectroscopy

The quantitative evaluation of the drug mixing condition was conducted for application in the forensic discrimination of drug powders using micro Fourier transform infrared (FT-IR) spectroscopy. Bromhexine hydrochloride (BHCl) and p-hydroxybenzoic acid (PHBA) were used as the simulated drug and additive, respectively. Equal masses of two chemicals were (1) simply mixed, (2) homogenized using agate mortar, or (3) dissolved in methanol and dried, and then (4) homogenized using agate mortar. The mixed powders dispersed on BaF₂ plates were subjected to mapping analysis of micro FT-IR spectroscopy using synchrotron radiation (SR) or globar light in transmission mode with aperture sizes of 2.5 x 2.5 and 10 x 10μm², and x-y scanning steps of 2.5 and 10 μm, respectively. The areas of the vibration bands specific to BHCl (C-N bending) and PHBA (C=O stretching) were converted to the molar contents (C_BHCl, C_PHBA), and the relative content ratio (RCR: C_PHBA/[C_BHCl + C_PHBA]) was used as one mixing parameter. The resulting two-dimensional distribution map provided the relative spatial localizations of the two species, and frequency histograms with a horizontal axis of RCR were plotted to evaluate the RCR distribution. The percentage frequency of the extreme value in which RCR was 0 or 1 (%EV) was used as one mixing index. After excluding the extreme values, the coefficient of variation (CV) of the RCR distribution was used as another mixing index. The differentiation among four mixing modes could be evaluated from the standpoint of %EV and CV, and the discrimination capacity by SR instrument was superior to that by globe light instrument.

Phenolic Profile and Fingerprint Analysis of Akebia quinata Leaves Extract with Endothelial Protective Activity

In contrast to the stem and fruit of Akebia quinata, A. quinata leaves as a source rich in phenolic compounds with potentially beneficial pharmacological activities have been largely overlooked. To develop and use A. quinata leaves as a resource, we evaluated its potential as a cardiovascular-protective agent. Herein, we investigated the effects and potential mechanisms of A. quinata leaves extract on lipopolysaccharide (LPS)-induced inflammatory responses in human umbilical vein endothelial cells. We found that A. quinata leaves extract pretreatment of 10 μg/mL significantly attenuated LPS-induced protein expression of intercellular adhesion molecule-1, vascular cell adhesion molecule-1. Furthermore, this extract also suppressed LPS-induced phosphorylation of nuclear factor-κB p65. In order to elucidate the chemical profiles of the samples, the HPLC fingerprint was established, and prominent peaks were identified via HPLC–electrospray ionization–mass spectrometry. Multivariate statistical analyses, including hierarchical cluster analysis, principal component analysis, and partial least-squares discriminant analysis, were performed to evaluate the clustering of the samples. It was found that isochlorogenic acid C was a key marker for the classification of A. quinata leaves from the Gongju and Muju city in Korea. Collectively, this study not only suggested the potential of A. quinata leaves as a novel therapeutic candidate for inflammatory cardiovascular disease but also developed a quality control method for A. quinata leaves, which could help to expand the application of A. quinata.

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.

Chemical Classification of Explosives

This work comprehensively reviews some fundamental concepts about explosives and their two commonly used classifications based on either their velocity of detonation or their application. These classifications are highly useful in the military/legal field, but completely useless for the chemical determination of explosives. Because of this reason, a classification of explosives based on their chemical composition is comprehensively revised, discussed and updated. This classification seeks to merge those dispersed chemical classifications of explosives found in literature into a unique general classification, which might be useful for every researcher dealing with the analytical chemical identification of explosives. In the knowledge of the chemical composition of explosives, the most adequate analytical techniques to determine them are finally discussed.

Infrared spectroscopy and spectroscopic imaging in forensic science

Infrared spectroscopy and spectroscopic imaging, are robust, label free and inherently non-destructive methods with a high chemical specificity and sensitivity that are frequently employed in forensic science research and practices. This review aims to discuss the applications and recent developments of these methodologies in this field. Furthermore, the use of recently emerged Fourier transform infrared (FT-IR) spectroscopic imaging in transmission, external reflection and Attenuated Total Reflection (ATR) modes are summarised with relevance and potential for forensic science applications. This spectroscopic imaging approach provides the opportunity to obtain the chemical composition of fingermarks and information about possible contaminants deposited at a crime scene. Research that demonstrates the great potential of these techniques for analysis of fingerprint residues, explosive materials and counterfeit drugs will be reviewed. The implications of this research for the examination of different materials are considered, along with an outlook of possible future research avenues for the application of vibrational spectroscopic methods to the analysis of forensic samples.

Influence of spectral resolution, spectral range and signal-to-noise ratio of Fourier transform infra-red spectra on identification of high explosive substances

In the contemporary spectroscopy there is a trend to record spectra with the highest possible spectral resolution. This is clearly justified if the spectral features in the spectrum are very narrow (for example infra-red spectra of gas samples). However there is a plethora of samples (in the liquid and especially in the solid form) where there is a natural spectral peak broadening due to collisions and proximity predominately. Additionally there is a number of portable devices (spectrometers) with inherently restricted spectral resolution, spectral range or both, which are extremely useful in some field applications (archaeology, agriculture, food industry, cultural heritage, forensic science). In this paper the investigation of the influence of spectral resolution, spectral range and signal-to-noise ratio on the identification of high explosive substances by applying multivariate statistical methods on the Fourier transform infra-red spectral data sets is studied. All mathematical procedures on spectral data for dimension reduction, clustering and validation were implemented within R open source environment.

Automated cervical precancerous cells screening system based on Fourier transform infrared spectroscopy features

Fourier transform infrared (FTIR) spectroscopy technique can detect the abnormality of a cervical cell that occurs before the morphological change could be observed under the light microscope as employed in conventional techniques. This paper presents developed features extraction for an automated screening system for cervical precancerous cell based on the FTIR spectroscopy as a second opinion to pathologists. The automated system generally consists of the developed features extraction and classification stages. Signal processing techniques are used in the features extraction stage. Then, discriminant analysis and principal component analysis are employed to select dominant features for the classification process. The datasets of the cervical precancerous cells obtained from the feature selection process are classified using a hybrid multilayered perceptron network. The proposed system achieved 92% accuracy.

A facile strategy applied to simultaneous qualitative-detection on multiple components of mixture samples: a joint study of infrared spectroscopy and multi-label algorithms on PBX explosives

We combined infrared spectroscopy with multi-label algorithms to propose a facile yet efficient strategy to realize simultaneous qualitative-detection on multiple components of mixture explosives without pre-separation.

Structural discrimination of nanosilica particles and mixed-structure silica by multivariate analysis applied to SAXS profiles in combination with FT-IR spectroscopy

New methodology to quickly identify changes in the structural properties of mesoporous silica materials through simultaneous multivariate analyses applied to techniques with different principles as SAXS curves and FT-IR spectra.

Optical filter selection for high confidence discrimination of strongly overlapping infrared chemical spectra

Optical filter-based chemical sensing techniques provide a new avenue to develop low-cost infrared sensors. These methods utilize multiple infrared optical filters to selectively measure different response functions for various chemicals, dependent on each chemical's infrared absorption. Rather than identifying distinct spectral features, which can then be used to determine the identity of a target chemical, optical filter-based approaches rely on measuring differences in the ensemble response between a given filter set and specific chemicals of interest. Therefore, the results of such methods are highly dependent on the original optical filter choice, which will dictate the selectivity, sensitivity, and stability of any filter-based sensing method. Recently, a method has been developed that utilizes unique detection vector operations defined by optical multifilter responses, to discriminate between volatile chemical vapors. This method, comparative-discrimination spectral detection (CDSD), is a technique which employs broadband optical filters to selectively discriminate between chemicals with highly overlapping infrared absorption spectra. CDSD has been shown to correctly distinguish between similar chemicals in the carbon-hydrogen stretch region of the infrared absorption spectra from 2800-3100 cm(-1). A key challenge to this approach is how to determine which optical filter sets should be utilized to achieve the greatest discrimination between target chemicals. Previous studies used empirical approaches to select the optical filter set; however this is insufficient to determine the optimum selectivity between strongly overlapping chemical spectra. Here we present a numerical approach to systematically study the effects of filter positioning and bandwidth on a number of three-chemical systems. We describe how both the filter properties, as well as the chemicals in each set, affect the CDSD results and subsequent discrimination. These results demonstrate the importance of choosing the proper filter set and chemicals for comparative discrimination, in order to identify the target chemical of interest in the presence of closely matched chemical interferents. These findings are an integral step in the development of experimental prototype sensors, which will utilize CDSD.

Detection of highly energetic materials on non-reflective substrates using quantum cascade laser spectroscopy

A quantum cascade laser spectrometer was used to obtain the reflection spectra of highly energetic materials (HEMs) deposited on nonideal, low-reflectivity substrates, such as travel-bag fabric (polyester), cardboard, and wood. Various deposition methods were used to prepare the standards and samples in the study. The HEMs used were the nitroaromatic explosive 2,4,6-trinitrotoluene (TNT), the aliphatic nitrate ester pentaerythritol tetranitrate (PETN), and the aliphatic nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). Chemometrics algorithms were applied to analyze the recorded spectra. Partial least squares (PLS) regression analysis was used to find the best correlation between the infrared signals and the surface concentrations of the samples, and PLS combined with discriminant analysis (PLS-DA) was used to discriminate, classify, and identity similarities in the spectral datasets. Several preprocessing steps were applied to prepare the mid-infrared spectra of HEMs deposited on the target substrates. The results demonstrate that the infrared vibrational method described in this study is well suited for the rapid screening analysis of HEMs on low-reflectivity substrates when a supervised model has been previously constructed or when a reference spectrum of the clean substrate can be acquired to be subtracted from the HEM-substrate spectrum.

Intelligent screening systems for cervical cancer

Advent of medical image digitalization leads to image processing and computer-aided diagnosis systems in numerous clinical applications. These technologies could be used to automatically diagnose patient or serve as second opinion to pathologists. This paper briefly reviews cervical screening techniques, advantages, and disadvantages. The digital data of the screening techniques are used as data for the computer screening system as replaced in the expert analysis. Four stages of the computer system are enhancement, features extraction, feature selection, and classification reviewed in detail. The computer system based on cytology data and electromagnetic spectra data achieved better accuracy than other data.

Multivariate statistical methods in the forensic investigation of the post-blast residues measured by Fourier transform infrared spectroscopy

Fourier transform infrared (FTIR) spectroscopy has gained significant attention among the forensic scientists because it shows high sensitivity and selectivity, and offers near-real-time detection. Application of the multivariate statistical techniques for the analysis of the spectra is necessary in order to enable feature extraction, proper evaluation and identification of obtained spectra. In this paper we show the development of a feasible procedure for the characterization of spectroscopic signatures of the explosive materials in the remnants after explosion. In our research especially designed and prepared sample catchers were used during the blasts of three various high explosives: C-4, TNT and PETN. Principal component analysis (PCA) was performed using broad spectral data range (600–4000 cm−1) for sample classification into separate classes. Most of the information contained in spectral data was compressed by PCA in few relevant principal components that explain most of the variance of spectral data. The results show that FTIR spectroscopy in combination with multivariate methods are well suited for identification and differentiation purposes even in very large data sets and could be employed by forensic laboratories for rapid screening analysis.