Soil forensics: A spectroscopic examination of trace evidence

Cigarette paper as evidence: Forensic profiling using ATR-FTIR spectroscopy and machine learning algorithms

This research highlights the underestimated significance of cigarette paper as evidence at crime scenes. The primary objective is to distinguish cigarette paper from similar-looking alternatives, addressing the first research objective. The second objective involves identifying cigarette paper brands using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and machine learning (ML) algorithms. Accurate differentiation of cigarette paper from normal paper is emphasized. ATR-FTIR spectroscopy, coupled with principal component analysis (PCA) for dimensionality reduction, is employed for brand identification. Among fifteen ML algorithms compared, the CatBoost classifier excels for both objectives. This research presents a non-destructive, effective method for studying cigarette paper, contributing valuable insights to crime scene investigations.

Feasibility study on identifying the source of cigarette ash based on infrared spectroscopy and chemometrics

Crime scene investigation is a key step in collecting and identifying physical evidence that may be closely related to the crime. The size of physical evidence can range from macro to micro. Cigarettes are a type of popular consumables, and their burned ashes are valuable resources of physical evidence since they contain important information such as brand preferences. This work explores the feasibility of using attenuated total reflection mid-infrared (ATR-MIR) spectroscopy and chemometrics to achieve cigarette brand recognition from burned ash. A total of 600 cigarette samples from ten brands were collected for experiments, and the samples were divided into a training set and a testing set in a 2:1 ratio. The Relief-F algorithm was used to sort variables and the forward search was used to further optimize variables to obtain the optimal subset of variables. Based on this, a partial least-squares discriminant analysis (PLS-DA) model was established, achieving a total accuracy of 97% on the test set. As a reference, the maximum correlation coefficient method was also used for classification, with an accuracy of only 73%. It seems that using the variable selection and modeling scheme proposed in this article is feasible for identifying cigarette brands from burned ash.

Advancing automobile identification and brand discrimination from tyre rubber through Machine learning algorithms for forensic investigations

Criminal instances involving collision accidents, hit-and-run incidents, abduction, hostage-taking, and the unauthorised transit of forbidden items generally include evidence involving rubber traces from automobile tyres. These traces can be located on the road surface, in clothing, on the victim(s) themselves, or on items as skid marks following sudden stopping and spinning around. These traces serve as crucial evidence by reducing the range of suspects by revealing linkages between the getaway vehicle, the site of the crime, and the perpetrator through the tyre's brand, producer, or origin. This study offered a way for classifying 220 tyre rubber samples from different brands using various machine learning algorithms in PyCaret in conjunction with rapid and non-destructive ATR-FTIR spectroscopy equipped with diamond crystal. On spectral information from ATR-FTIR, pre-processing tools such as baseline correction, smoothing, derivatization, and normalisation were also implemented prior to machine learning. This approach has the potential to be advantageous for efficiently and non-destructively identifying rubber traces as forensic evidence and for facilitating brand recognition of automobile tyres.

Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms

The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with machine learning. ATR-FTIR offers non-destructive and non-invasive advantages, requiring minimal sample preparation. By detecting specific chemical bonds in blood components, it enables the differentiation of various body fluids. However, the subjective interpretation of the spectra poses challenges in distinguishing different fluids. To address this, we employ machine learning techniques. Machine learning is extensively used in chemometrics to analyze chemical data, build models, and extract useful information. This includes both unsupervised learning and supervised learning methods, which provide objective characterization and differentiation. The focus of this study was to identify human and porcine blood on substrates using ATR-FTIR spectroscopy. The substrates included paper, plastic, cloth, and wood. Data preprocessing was performed using Principal Component Analysis (PCA) to reduce dimensionality and analyze latent variables. Subsequently, six machine learning algorithms were used to build classification models and compare their performance. These algorithms comprise Partial Least Squares Discriminant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performance of different identification models still varies based on the type of substrate. The integration of these disciplines enables researchers to harness the power of data-driven approaches for solving complex forensic problems. The objective differentiation of bloodstains using machine learning holds significant implications for criminal investigations. This technique offers a non-destructive, simple, selective, and rapid approach for forensic analysis, thereby assisting forensic scientists and investigators in determining crucial evidence related to bloodstains.

The Development of Honey Recognition Models Based on the Association between ATR-IR Spectroscopy and Advanced Statistical Tools

The newly developed prediction models, having the aim to classify Romanian honey samples by associating ATR-FTIR spectral data and the statistical method, PLS-DA, led to reliable differentiations among the samples, in terms of botanical and geographical origin and harvesting year. Based on this approach, 105 out of 109 honey samples were correctly attributed, leading to true positive rates of 95% and 97% accuracy for the harvesting differentiation model. For the botanical origin classification, 83% of the investigated samples were correctly predicted, when four honey varieties were simultaneously discriminated. The geographical assessment was achieved in a percentage of 91% for the Transylvanian samples and 85% of those produced in other regions, with overall accuracy of 88% in the cross-validation procedure. The signals, based on which the best classification models were achieved, allowed the identification of the most significant compounds for each performed discrimination.

Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy Approach

Synthetic fibers are one of the most valuable trace lines of evidence that can be found in crime scenes. When textile fibers are analyzed properly, they can help in finding a linkage between suspect, victim, and the scene of the crime. Various analytical techniques are used in the examination of samples to determine relationships between different fabric fragments. In this exploratory study, multivariate statistical methods were investigated in combination with machine learning classification models as a method for classifying 138 synthetic textile fibers using Fourier transform infrared spectroscopy, FT-IR. The data were first subjected to preprocessing techniques including the Savitzky–Golay first derivative method and Standard Normal Variate (SNV) method to smooth the spectra and minimize the scattering effects. Principal Component Analysis (PCA) was built to observe unique patterns and to cluster the samples. The classification model in this study, Soft Independent Modeling by Class Analogy (SIMCA), showed correct classification and separation distances between the analyzed synthetic fiber types. At a significance level of 5%, 97.1% of test samples were correctly classified.

A case study in forensic soil comparison

Soil examination can provide useful forensic information about the spatial location and suspect's activities. Many techniques have been applied for soil comparison and provenance determination in criminal investigations. Pollen and diatom identification, which has the potential to provide an independent ecological assessment of soil evidence, is currently underused in forensic soil analysis. This work presents a case study of application of these methods to help criminal investigation in a murder case, which happened in an irrigation ditch in Hunan Province, southern China. Soils from the suspect's clothes, the exact crime scene spot in the irrigation ditch, along the ditch and the reference ditches were collected and analyzed. In addition to the element and mineral analysis, pollen and diatom assemblages were analyzed for further comparison. The statistical methods of hierarchical cluster and cosine similarity analysis were carried out to assist in soil comparison and provenance determination. The results showed that soil on the suspect's clothes had a high probability to share the same source with the soil from the crime scene in the irrigation ditch. The suspect confessed to murder based largely on the soil examination result even without other evidences.

Geochemical fractionation and spectroscopic fingerprinting for evaluation of the environmental transformation of potentially toxic metal(oid)s in surface-subsurface soils

The contamination of soil by toxic metal(oid)s has emerged as a major concern worldwide, particularly in developing countries. A metals behavior in the soil environment is influenced by organic matter, mineral phases, and oxidation states in which a particular metal exists. However, the spectroscopic evidence of metal(oid)s interactions in soil with organic matter and mineral phases can induce an extensive understanding. The surface and sub-surface soils (0-50 cm) from four sites of upper Indus basin, Pakistan, were collected and analyzed by using FTIR (Fourier-transform infrared spectroscopy), XRD (X-ray diffraction) and XPS (X-ray photoelectron spectroscopy) in addition to ICP-MS (inductively coupled plasma mass spectrometry) and geochemical fractionation. Geochemical fractionation of metal(oid)s indicated that As, Cu, Ni, Pb, and Zn were mostly found in the potentially bioavailable fractions. However, an increase in the residual fraction was observed from top to bottom. The absorption bands of FTIR spectra were divided into three spectral regions 700-400, 1700-800, and 3700-2800 cm^-1. The soil was found rich in organic matter and capable of retaining metals as abundant peaks were observed in the mid-infrared region. The mineralogical analysis of soil samples testified silicon oxides and zeolite as major mineral phases. The XPS spectra showed broad peaks of As(III), As₂O₃, As₄S₄, PbO₂, and PbCo₃. The study concludes that the source identification of metal(oid)s in the upper Indus is crucial to find out the particular source of contamination in the soil.

A rapid and non-destructive ATR-FTIR spectroscopy method supported by chemometrics for discriminating between facial creams and the classification into herbal and non-herbal brands

Facial creams are considered to be essential beauty items and are used by both females and males on an everyday basis. These can be encountered as an evidentiary material in criminal investigations, particularly in cases related to sexual and physical assaults against women. These are found in trace amounts and therefore their analysis is difficult and also, it must be through non-destructive methods. In the present work ATR-FTIR spectroscopy was employed for the discrimination of 57 samples of face creams out of which 31 were non-herbal and 26 were from herbal category. Visual analysis of the obtained Spectra was done for discrimination purposes but the method was prone to human error and laborious too. The spectroscopic results were analyzed with PCA (Principal Component Analysis) and PLS-DA (Partial least square discriminant analysis) methods. A segregation of samples was seen in the PCA plots to some extent. The class separation and prediction of the samples was performed using PLS-DA method. A good classification was achieved between herbal and non-herbal samples using PLS-DA method. Further, validation of the model was also performed by testing 10 unknown samples.

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.

Evaluating Cr behaviour in two different polluted soils: Mechanisms and implications for soil functionality

This work investigates the mechanisms determining Cr speciation and availability in two different soils polluted with two chromium sources (an industrial sludge, highly polluted with Cr, and Cr(VI) solution) and the influence of these parameters on the recovery of the soil functions related with biological quality and plant growth. The experiment was carried out in greenhouse conditions using 36 pots of 17 kg for the growth of Silene vulgaris for 21 months. Logistic Regression Model using Lasso estimator shows that soil organic matter (SOM) and pH control Cr availability in studied soils. In soils treated with the sludge, X ray Absorption spectroscopy showed that Cr was present as Cr(III), biological quality indicators increased and plants were able to grow. However, in soils polluted with Cr(VI), Cr availability was significantly different in the two soils. In the alkaline and poor in organic matter soil, 12% of Cr(VI) remained in the soil leading to the decrease of soil quality indicators and the total inhibition of plant growth. In the neutral soil, Cr(VI) was totally reduced to Cr(III) by soil organic matter (SOM), quality indicators were not affected and plants grown properly. Infrared Spectroscopy showed that different functional groups reacted with Cr in the two soils. This study highlights the importance to understand the mechanisms underlaying Cr redox and adsorption reactions in Cr polluted soils as they determine the potential recovery of the functions related with biological quality indicators and plant growth. The methodology proposed allows this study in complex soil samples at realistic concentrations and may be useful for risk assessment and for the planning of managing strategies in Cr polluted soils.

On the discrimination of soil samples by derivative diffuse reflectance UV-vis-NIR spectroscopy and chemometric methods

The derivative diffuse reflectance UV-vis-NIR spectroscopy combined with the multivariate methods are utilized for the discrimination and classification of the soil samples collected from the north-western part of India. The acquired spectra reveal the presence of different organic and inorganic minerals such as humic acid, fulvic acid, hematite, etc. in varying amounts. The differentiation/segregation among soil samples is achieved by peak comparison and chemometric methods like clustering algorithm and principal component analysis (PCA). Among these, the PCA method gives clear discrimination of soil samples. The developed PCA model is further validated by analyzing unknown samples for the prediction to their respective clusters significantly. Principal component linear discriminant analysis (PC-LDA) based discriminant model is developed to classify the unknown soil samples to its respective groups. PC-LDA based model reveals 95 % accurate clustering of the soil by the leave-one-out cross-validation approach.

On the IR spectroscopy and chemometric based rapid and non-destructive method for the investigation of sunscreen stains: Application in forensic science

Cosmetic products such as sunscreens may often be encountered in forensic investigations as traces left on tissue paper, apparels, drinkware, painted exterior, or various alternative surfaces. The pilot study aimed at efficiently using Infrared spectroscopy coupled with chemometrics to cater to unbiased, rapid, and non-destructive identification of sunscreens which will aid various forensic investigations soon. In the present research, a total of 109 sunscreen samples were analyzed using Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy. The physicochemical data from the FTIR instrument was then subjected to principal component analysis (PCA), which successfully distinguished most of the samples based on their spectral information. The trained model resulted in clear segregation of unknown sunscreen samples. This could provide an outstanding level of confidence during the conduction of 'questioned versus known' comparisons of similar sunscreens and will give the details of the manufacturer, thereby, helping in rounding off the suspects.

Soil as a tool of revelation in forensic science: a review

Soil contains diverse and complex natural elements having physical, chemical, mineralogical and biological components. Soil being a transferable physical component (it can be transferred from one location to another with the help of shoes, tires, clothes, tools etc.), acts as a tool of forensic investigation to correlate a specific crime scene with criminal suspects. A variety of techniques and combinations of methods can be used to discriminate soil from different geographical locations. The present review highlights various analytical techniques (ATR-FTIR, pyGC-MS, SEM-EDX, ICP-MS/OES and XRD) for soil analysis (colour comparison, texture and particle size determination, density gradient methods and organic matter estimation) and discusses some of the famous cases solved with soil trace evidence. The objective of the present study is to provide an overview of the importance of soil as physical evidence in forensic science based on literature analysis that will help forensic scientists and researchers to select appropriate methods to discriminate different soil samples. This article reviews various analytical techniques used to differentiate soils and provides compiled information regarding soil as trace evidence in order to help academicians, researchers and forensic soil scientists.

Discrimination of vermilion (sindoor) using attenuated total reflectance fourier transform infrared spectroscopy in combination with PCA and PCA-LDA

Vermilion (sindoor) is considered sacred in the Hindu religion, and it is used routinely throughout the world by married Hindu women along the line of hair parting during marriage ceremonies, religious rituals, and festivals. Owing to its esthetic appeal, it is sometimes illegally used as a food additive; leading to potential health risks. Therefore, due to the aforementioned reasons, vermilion can likely be encountered as trace evidentiary material during crime investigations, particularly in cases of sexual and physical offenses against women. Analysis of such evidence can provide a link between the criminal, the victim, and the crime scene and thereby be utilized as associative evidence in the court of law. In the present study, ATR-FTIR spectroscopy has been used for the examination of 37 different manufacturers of vermilion. Chemometric methods such as principal component analysis (PCA) and PCA-LDA were performed on the obtained spectra for objective interpretation of results. PCA delivered 99.06% discrimination of samples while PCA-LDA employed for classification purpose delivered 95.25% calibration accuracy and 88% validation accuracy. Afterward, the validity of the chemometric methods employed was tested by blind testing of samples. A preliminary study on the effect of selected substrates (cotton cloth, tissue paper, glass, and plastic) on sample analysis indicates that while sample stain on substrates could be linked to its parent source even after a month, linking an aged samples (after 8 months) could be hindered due to evaporation of components present in vermilion. Overall, the current methodology utilized has a potential prospect in future forensic-casework.

Mid- to long-wave infrared computational spectroscopy with a graphene metasurface modulator

In recent years there has been much interest concerning the development of modulators in the mid- to long-wave infrared, based on emerging materials such as graphene. These have been frequently pursued for optical communications, though also for other specialized applications such as infrared scene projectors. Here we investigate a new application for graphene modulators in the mid- to long-wave infrared. We demonstrate, for the first time, computational spectroscopy in the mid- to long-wave infrared using a graphene-based metasurface modulator. Furthermore, our metasurface device operates at low gate voltage. To demonstrate computational spectroscopy, we provide our algorithm with the measured reflection spectra of the modulator at different gate voltages. We also provide it with the measured reflected light power as a function of the gate voltage. The algorithm then estimates the input spectrum. We show that the reconstructed spectrum is in good agreement with that measured directly by a Fourier transform infrared spectrometer, with a normalized mean-absolute-error (NMAE) of 0.021.

Forensic soil analysis using laser-induced breakdown spectroscopy (LIBS) and Fourier transform infrared total attenuated reflectance spectroscopy (FTIR-ATR): Principles and case studies

Soils are crucial trace evidence that can establish or exclude the relationship between a suspect, victim, or an object at a particular scene, which could contribute to building a case. Laser-induced breakdown spectroscopy (LIBS) and Fourier transform infrared-attenuated total reflectance (FTIR-ATR) spectroscopy have been demonstrated to be effective techniques for soil characterization owing to its being rapid, non-destructive, and convenient analysis with little sample preparation requirements. Therefore, the principles of LIBS and FTIR-ATR techniques for soil forensic analysis in typical soil samples were investigated and their practical feasibility was tested by applying the techniques to forensic soil samples in two criminal cases. Principal component analysis (PCA) of a typical soil sample indicated that five typical soil types were clearly distinguished by LIBS and FTIR-ATR spectra. Variations in the soil elements (i.e., Si, Mg, Al, Ca, K, O, and N) and functional groups (i.e., OH/NH, CC/CO, SiO, CO₃^2-, AlOH, and NH₂) are crucial indicators for soil identification. The casework results demonstrated that both LIBS and FTIR-ATR show great potential for forensic soil analysis in future cases.

Mid- to long-wave infrared computational spectroscopy using a subwavelength coaxial aperture array

Miniaturized spectrometers are advantageous for many applications and can be achieved by what we term the filter-array detector-array (FADA) approach. In this method, each element of an optical filter array filters the light that is transmitted to the matching element of a photodetector array. By providing the outputs of the photodetector array and the filter transmission functions to a reconstruction algorithm, the spectrum of the light illuminating the FADA device can be estimated. Here, we experimentally demonstrate an array of 101 band-pass transmission filters that span the mid- to long-wave infrared (6.2 to 14.2 μm). Each filter comprises a sub-wavelength array of coaxial apertures in a gold film. As a proof-of-principle demonstration of the FADA approach, we use a Fourier transform infrared (FTIR) microscope to record the optical power transmitted through each filter. We provide this information, along with the transmission spectra of the filters, to a recursive least squares (RLS) algorithm that estimates the incident spectrum. We reconstruct the spectrum of the infrared light source of our FTIR and the transmission spectra of three polymer-type materials: polyethylene, cellophane and polyvinyl chloride. Reconstructed spectra are in very good agreement with those obtained via direct measurement by our FTIR system.

On the spectroscopic investigation of lipstick stains: Forensic trace evidence

In forensic science, lipsticks are considered as crucial trace evidence because it helps in the linking of the criminal with the crime scene. In the present work, twenty-five lipstick samples are characterized and discriminated by using ATR-FTIR spectroscopy coupled with multivariate statistical methods. The utilized approach is non-destructive, fast, and provides reproducible results. It is observed from the FTIR spectra that lipstick contains various aliphatic and aromatic compounds e.g. Propyl ester of Hexanoic acid, Silicates, etc. Further, the discrimination power is calculated by using three approaches i.e. visual examination, cluster analysis (HCA and k-means) and factor analysis method. The multivariate method combined with t-statistics delivered a higher value of discriminating power i.e. 100% which is an improvement on the 99.00% discrimination power of visual comparison method. The developed method is validated by analyzing five duplicate samples and predicted them to their respective brands significantly. This study establishes a method which provides proof of concept discrimination of the lipstick samples. In the future, it can be quite possible to create an FTIR database of more lipstick samples for the identification of unknown/suspected lipstick samples.

Authenticity identification and classification of Rhodiola species in traditional Tibetan medicine based on Fourier transform near-infrared spectroscopy and chemometrics analysis

Rhodiola is an increasingly widely used traditional Tibetan medicine and traditional Chinese medicine in China. The composition profiles of bioactive compounds are somewhat jagged according to different species, which makes it crucial to identify authentic Rhodiola species accurately so as to ensure clinical application of Rhodiola. In this paper, a nondestructive, rapid, and efficient method in classification of Rhodiola was developed by Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics analysis. A total of 160 batches of raw spectra were obtained from four different species of Rhodiola by FT-NIR, such as Rhodiola crenulata, Rhodiola fastigiata, Rhodiola kirilowii, and Rhodiola brevipetiolata. After excluding the outliers, different performances of 3 sample dividing methods, 12 spectral preprocessing methods, 2 wavelength selection methods, and 2 modeling evaluation methods were compared. The results indicated that this combination was superior than others in the authenticity identification analysis, which was FT-NIR combined with sample set partitioning based on joint x-y distances (SPXY), standard normal variate transformation (SNV) + Norris-Williams (NW) + 2nd derivative, competitive adaptive reweighted sampling (CARS), and kernel extreme learning machine (KELM). The accuracy (ACCU), sensitivity (SENS), and specificity (SPEC) of the optimal model were all 1, which showed that this combination of FT-NIR and chemometrics methods had the optimal authenticity identification performance. The classification performance of the partial least squares discriminant analysis (PLS-DA) model was slightly lower than KELM model, and PLS-DA model results were ACCU = 0.97, SENS = 0.93, and SPEC = 0.98, respectively. It can be concluded that FT-NIR combined with chemometrics analysis has great potential in authenticity identification and classification of Rhodiola, which can provide a valuable reference for the safety and effectiveness of clinical application of Rhodiola.