Chemometrics in forensic science: approaches and applications

Species discrimination from hair using ATR-FTIR spectroscopy: Application in wildlife forensics

Hair is a commonly encountered trace evidence in wildlife crimes involving mammals and can be used for species identification which is essential for subsequent judicial proceedings. This proof of concept study aims, to distinguish the black guard hair of three wild cat species belonging to the genus Panthera i.e. Royal Bengal Tiger (Panthera tigris tigris), Indian Leopard (Panthera pardus fusca), and Snow Leopard (Panthera uncia) using a rapid and non-destructive ATR-FTIR spectroscopic technique in combination with chemometrics. A training dataset including 72 black guard hair samples of three species (24 samples from each species) was used to construct chemometric models. A PLS2-DA model successfully classified these three species into distinct classes with R-Square values of 0.9985 (calibration) and 0.8989 (validation). VIP score was also computed, and a new PLS2DA-V model was constructed using variables with a VIP score ≥ 1. External validation was performed using a validation dataset including 18 black guard hair samples (6 samples per species) to validate the constructed PLS2-DA model. It was observed that PLS2-DA model provides greater accuracy and precision compared to the PLS2DA-V model during cross-validation and external validation. The developed PLS2-DA model was also successful in differentiating human and non-human hair with R-Square values of 0.99 and 0.91 for calibration and validation, respectively. Apart from this, a blind test was also carried out using 10 unknown hair samples which were correctly classified into their respective classes providing 100 % accuracy. This study highlights the advantages of ATR-FTIR spectroscopy associated with PLS-DA for differentiation and identification of the Royal Bengal Tiger, Indian Leopard, and Snow Leopard hairs in a rapid, accurate, eco-friendly, and non-destructive way.

Chemical analysis of polymers used for 3D printing of firearms

3D-printed firearms cause challenges in criminal investigations and forensic analysis because they are difficult to trace. Indeed, in addition to being "ghost guns", they may not produce all the conventional ballistic traces normally used for firearm identification. However, 3D-printed firearms produce other very specific traces, such as polymer traces which come from the polymers used to print the firearm. To date, only a few studies have focused on the analysis of polymer traces. This study therefore aims to characterize polymer traces from 3D-printed firearms, using non-destructive spectroscopic techniques readily available in most forensic laboratories (i.e., FTIR and Raman) and evaluate the potential for association of polymer specimens or traces with their source. To do so, the study was divided into four parts. First, the population study conducted among 3D printing companies and individuals practicing 3D printing has revealed that PLA and PLA+ are the most widely used polymer types in Quebec, Canada. Second, FTIR and Raman spectroscopic analysis of polymer samples collected during the population study has allowed the development of a reference polymer spectral database. The analysis and interpretation of these spectra revealed that polymer filaments present very low intravariability, but very high intervariability, due in part to the different polymer types and the pigments used to color them. The use of chemometric tools with the spectra showed that these two spectroscopic methods were highly discriminating. Third, test firing of 3D-printed firearms has allowed for the simulation of a scene involving this type of firearm and the collection of polymer traces generated. Fourth, the comparison of chemical signatures between polymer filaments and polymer traces has allowed for the evaluation of the potential for chemical association. This study highlights the added value of chemical analysis of 3D-printed firearms polymer traces in a criminal investigation by demonstrating that polymer filaments, the polymer from which a 3D-printed firearm is made, as well as polymer traces generated during firing, can be linked chemically and provide relevant information.

Rapid and non-destructive approach for characterization and differentiation of sealing wax using ATR-FTIR spectroscopy

Sealing wax is used for maintaining the integrity and authenticity of a document or physical evidence. Any tampering with the seal calls into question the overall integrity and authenticity of the tangible evidence or document. In these circumstances, determining the authenticity of the sealing material (physical and chemical) becomes imperative. In this study, ATR-FTIR spectroscopy supported by chemometrics has been used to differentiate sealing wax samples belonging to 12 different brands available across India. All the samples were first melted, cooled, and then analyzed using ATR-FTIR spectroscopy in the mid-infrared region (4000-600 cm-1 ). The obtained spectra were first examined visually for the presence of different functional groups. Principal component analysis (PCA) and principal component analysis-linear discriminant analysis (PCA-LDA) were employed to analyze the sample clustering patterns and to categorize them into their respective groups, respectively. For classification, a PCA-LDA training model was applied, and it demonstrated 95.83% accuracy. The validation test resulted in an accuracy of 83.33%. PCA-LDA model offered 100% accurate prediction for samples on various substrates, including cloth, cardboard, and paper. A blind study was also performed using five unknown samples, which were accurately classified into their respective groups. PCA-LDA model will be helpful in providing investigative leads by linking a questioned sealing wax sample with its respective group.

A comprehensive ATR-FTIR spectroscopic analysis for the identification and differentiation of lip balms

Lip balm may be encountered as physical evidence in cases involving sexual assaults, homicides, and kidnappings. Lip balm can be used as corroborative evidence by providing a potential link between the victim, accused, and the crime scene. For lip balms to be used as evidence, it is important to understand the diversity and their aging process under different conditions. Therefore, in this study, ATR-FTIR spectroscopy in conjunction with chemometric tools such as principal component analysis (PCA) and linear discriminant analysis (LDA) has been used for the objective identification and differentiation of 20 brands of lip balms. Moreover, lip balms on different substrates and wearing effects over time were also investigated. The results show that the PCA-LDA training accuracy was 92.5%, whereas the validation accuracy comes out to be 83.33%. A blind study using pristine samples was also performed which resulted in 80% PCA-LDA accuracy. PCA-LDA prediction of samples on various substrates showed a higher chemometric prediction accuracy for nonporous substrates (glass, plastic, and steel), than for porous substrates (cotton cloth, cotton swab stick, dry tissue paper, and white paper) for samples kept in room temperature and under sunlight for 15 days. The substrate study showed that the samples from various substrates could effectively generate respective spectra which can help in brand-level identification even after several days. The present method demonstrates a potential for lip balm samples to be used in forensic casework applications.

Applications of Raman spectroscopy in the analysis of biological evidence

During the past few decades, Raman spectroscopy has progressed and captivated added attention in the field of science. However, the application of Raman spectroscopy is not limited to the field of forensic science and analytical chemistry; it is one of the emerging spectroscopic techniques, utilized in the field of forensic science which in turn could be a supporting tool in the law and justice system. The advantage of Raman spectroscopy over the other conventional techniques is that it is rapid, reliable, and non-destructive in nature with minimal or no sample preparation. The quantitative and qualitative analysis of evidence from biological and non-biological origins could easily be performed by using Raman spectroscopy. The forensic domain is highly complex with multidisciplinary branches, and therefore a plethora of techniques are utilized for the detection, identification, and differentiation of innumerable pieces of evidence for the purpose of law and justice. Herein, a systematic review is carried out on the application of Raman spectroscopy in the realm of forensic biology and serology considering its usefulness in practical perspectives. This review paper highlights the significance of modern techniques, including micro-Raman spectroscopy, confocal Raman spectroscopy, surface-enhanced Raman spectroscopy, and paper-based surface-enhanced Raman spectroscopy, in the field of Raman spectroscopy. These techniques have demonstrated notable advancements in terms of their applications and capabilities. Furthermore, to comprehensively capture the progress in the development of Raman spectroscopy, all the published papers which could be retrieved from the available databases from the year 2007 to 2022 were incorporated.

A comprehensive quality evaluation method of different medicinal parts of Physalis Calyx seu Fructus by fingerprints, chemometrics, antioxidant activity, network pharmacology and molecular docking

Physalis Calyx seu Fructus (PCF) is a herb widely used in China for its function of clearing heat and detoxifying, benefitting the pharynx and reducing phlegm, both in health care and in tea drinking. However, the quality of its fruit and calyx is uneven and the storage period is short. Therefore, it is crucial to develop other parts of PCF with longer storage periods and obvious medicinal effects. Firstly, high-performance liquid chromatography was used to develop the fingerprint of different parts of PCF, and various chemometric analyses were conducted to screen out chemical markers. The calyxes of PCF were found to cluster together, distinct from the fruits, roots, stems and leaves. The active components of PCF were concentrated in the persistent calyxes, and flavonoids were mainly found in the persistent calyxes and leaves. Secondly, the extraction of persistent calyxes showed the strongest scavenging ability of DPPH and ABTS. Finally, the important chemical markers were verified by network pharmacological analysis and molecular docking. It provides a reference for the clinical application of PCF, and the obtained chemical markers offer a scientific basis for quality evaluation.

Chemical Analysis of Gunpowder and Gunshot Residues

The identification of firearms is of paramount importance for investigating crimes involving firearms, as it establishes the link between a particular firearm and firearm-related elements found at a crime scene, such as projectiles and cartridge cases. This identification relies on the visual comparison of such elements against reference samples from suspect firearms or those existing in databases. Whenever this approach is not possible, the chemical analysis of the gunpowder and gunshot residue can provide additional information that may assist in establishing a link between samples retrieved at a crime scene and those from a suspect or in the identification of the corresponding model and manufacturer of the ammunition used. The most commonly used method for the chemical analysis of gunshot residue is scanning electron microscopy with energy dispersive X-ray, which focuses on the inorganic elements present in ammunition formulation, particularly heavy metals. However, a change in the legal paradigm is pushing changes in these formulations to remove heavy metals due to their potential for environmental contamination and the health hazards they represent. For this reason, the importance of the analysis of organic compounds is leading to the adoption of a different set of analytical methodologies, mostly based on spectroscopy and chromatography. This manuscript reviews the constitution of primer and gunpowder formulations and the analytical methods currently used for detecting, characterising, and identifying their compounds. In addition, this contribution also explores how the information provided by these methodologies can be used in ammunition identification and how it is driving the development of novel applications within forensic ballistics.

The choice of spectral similarity algorithms influences suspected soil sample provenance

Similarity algorithms are commonly used in soil forensic applications to help identify similar samples from an existing reference library as possible source locations of unknown target samples. These algorithms are well-suited to compare soil spectra. However, different similarity algorithms may lead to different clusters of similar samples, and thus different strengths of evidence in forensic investigations. To quantify this, we conducted a study to evaluate the influence of seven similarity algorithms on soil provenance, using as a sample set a soil spectral library consisting of 280 soil profiles from Anhui Province, China. This library includes three spatial scales of datasets: provincial (DS_p), county (DS_c) and field (DS_f). A set of ten samples covering a wide range of spectra variations were selected from the DS_f dataset as the "unknown" samples, with the remaining being used as the reference samples. This study aimed to: (1) evaluate how several commonly-used similarity algorithms, namely Euclidean distance (ED), Mahalanobis distance (MD), Spectral angle mapper (SAM), and Spectral information divergence (SID), as well as variants of several of these measured in standardized principal component space computed from the spectra (ED_PCA, MD_PCA and SAM_PCA), influence the identification of the matched similar samples; (2) determine the overlap in sample selection between different similarity algorithms; (3) propose best practices for similarity algorithms applied to soil forensic analysis using spectroscopy. The use of different similarity algorithms did influence the selection of most similar samples. The similarity algorithms calculated in PC space (ED_PCA, MD_PCA and SAM_PCA) performed slightly better than their counterparts calculated in spectral space. Due to the availability of a detailed spectral library, regardless of the different similarity algorithms used, the matched most similar samples were all located close to the unknowns, mostly within 3 km, with one exception. That is, the varied choices of different similarity algorithms hardly influenced the conclusion of soil provenance in this case. In general, MD_PCA, SAM and ED were the best similarity algorithms overall. However, since there was no single best algorithms for all cases, we recommend the joint use of MD_PCA, SAM and ED as an ensemble. Indications of possible sample provenance from these similarity measured can be useful evidence to complement evidence from other methods in a forensic investigation.

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.

Detection of Plastic Granules and Their Mixtures

Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400-1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400-1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used.

Chemometrics in Forensic Chemistry - Part III: Quality assessment and interpretation of chemometric output

The number of samples sent to forensic laboratories as well as the complexity of the drug situation has increased tremendously during recent years. At the same time the amount of data gathered from chemical measurements has been mounting. This creates challenges for forensic chemists: how to handle the data, how to reliably answer the questions asked, and how to examine the data to find new properties or how to disclose connections with respect to source attribution of samples within a case or retrospective to past cases, stored in a database. Previously published articles Chemometrics in Forensic Chemistry - Part I and II discussed where in the forensic workflow of routine casework chemometrics is applied, and presented examples of chemometric methods used in cases of illicit drugs. This article explains through examples that the chemometric results must never stand-alone. Before such results are reported, quality assessment steps, which may consist of operational, chemical, and forensic assessments are required. In each case a forensic chemist needs to consider the suitability of chemometric methods, based on their strengths, weaknesses, opportunities and threats (SWOT). This is because while chemometric methods are powerful tools managing complex data, they are to some extent chemically blind.

Discrimination of electrical tapes using ATR-FTIR spectroscopy and chemometrics

Electrical tapes are recovered during criminal investigations as physical evidence in cases of rape, kidnapping, and explosion incidents. The analysis of such evidence can provide an evidentiary link between the suspect, the victim, object, or the crime scene. In the present study, 25 brands of electrical tapes have been analyzed using ATR-FTIR (attenuated total reflectance Fourier transform infrared) spectroscopy. Samples (1 cm² ) were analyzed in the mid IR (Infrared) region from 4000-600 cm^-1 , and the functional groups of various components have been profiled. Chemometric methods-PCA (principal component analysis) and PCA-LDA (linear discriminant analysis) have been employed to interpret the data and classify the samples into its respective classes. Preliminary assessment of sample clustering due to similar chemical composition was visualized using PCA. PCA-LDA applied for classification purpose yielded classification accuracy (calibration) of 92.98% for the adhesive side and 88% for the backing side. The validation results showed classification accuracy of 89.47% for the adhesive side and 84% for the backing side. Blind validation study was carried out using 5 samples, and classification accuracy of 100% and 80% was obtained for the adhesive and the backing side, respectively. In the current study, a preliminary substrate study was carried out, and the results showed that the backing samples could be more accurately matched to their correct source of origin than the adhesive side.

3D imaging of single bacterial cells using surface-enhanced Raman spectroscopy with a multivariate curve resolution model

Imaging biomolecules within a single bacterial cell is crucial for understanding cellular genetic mechanisms. Herein, we exploited a surface-enhanced Raman spectroscopy (SERS) imaging strategy for single cell analysis. Cellular biosynthesized Ag nanoparticles (NPs) provided the necessary enhancement for SERS imaging. Multiple complementary techniques, including high-resolution transmission electron microscopy (HR-TEM), high-angle annular dark-field (HAADF)-scanning transmission electron microscopy (STEM), and energy-dispersive X-ray spectroscopy (EDX), were used to characterize the biogenic Ag NPs in cells. Three-dimensional SERS imaging maps displayed spectral information of biomolecules within the single cell. The multivariate curve resolution (MCR) model and principal component analysis (PCA) model were used to analyze the cellular SERS imaging maps. The MCR model, with a specific constraint of non-negativity, resulted in meaningful identification of biomolecules associated with Ag reduction. Focusing on the molecular level reveals that Pantoea sp. IMH utilizes several mechanisms to synthesize Ag NPs, including cytoplasm reduction by glucose or nicotinamide adenine dinucleotide (NADH)-dependent reductase, and extracellular reduction by an electron transfer chain containing quinone and cytochrome C. Our results shed new light on the Ag NP biosynthesis mechanism and single cell Raman analysis.

Writing sequence identification of seals and signatures in documents using ambient mass spectrometry imaging with chemometric methods

Identifying the writing sequence of seals and signatures in documents is often performed and difficult to resolve in forensic determination. Morphological and physical-chemical analysis methods are often limited by the destructive nature of samples, a high signal response strength and specific materials. Mass spectrometry imaging (MSI) has been used as an alternative method because it can generate molecular images from many surfaces and produce rich chemical information. Herein, we developed a sequence identification method by coupling an air flow-assisted desorption electrospray ionization (AFADESI)-MSI system with a chemometric analysis, which can holistically and directly analyse document samples under ambient, moderate and selectable conditions and maintain the original appearance of the paper documents after sampling. By integrating principal component analysis (PCA) and the partial least squares discriminant analysis (PLS-DA), equivocal point analysis can be objectively performed, where knowing the components of the seal or signature is not necessary to identify the sequence. In total, 28 prepared samples with known sequences and two original blind test samples were analysed. One prepared sample was analysed in negative ionization mode, and other samples were inferred in positive ionization mode. All writing sequences were in accordance with the actual case. The writing sequence of the blind testing of the original samples was correctly identified. This study provided a convenient, objective and quasi-nondestructive method to investigate the sequence differences among equivocal document samples and is promising for providing an alternative method for the sequence identification of seals and signatures in questionable documents.

Rapid and non-destructive analysis of eye-cosmetics using ATR-FTIR spectroscopy and chemometrics

Cosmetic evidence recovered during crime investigations, particularly in cases of physical and sexual assault against women can be utilised as associative evidence in the court of law. This evidence can provide a link between the suspect, the victim, and the crime scene and assist in solving criminal cases. A mismatched profile of exhibit's source of origin can also be utilised to definitely exclude the suspect exhibits. In the present study, ATR-FTIR (attenuated total reflectance-fourier transform infrared) spectroscopy has been employed for the analysis of eye-cosmetics (eyeliner and eyeshadow) samples. Chemometric tool- PCA (principal component analysis) has been used for the recognition of patterns in the data. PCA-LDA (linear discriminant analysis) utilized for classification purpose showed calibration accuracy of 100% and 98% for eyeliner and eyeshadow respectively while validation result showed 97% and 97% respectively. Preliminary substrate study has been performed in the current study. Result suggests that substrates such as cotton cloth and tissue paper hinder the analysis of eyeliner while the stain of eyeshadow on substrates such as cotton cloth, tissue paper, glass, and plastic could be correctly matched with its parent source.

Revealing Unknown Controlled Substances and New Psychoactive Substances Using High-Resolution LC-MS/MS Machine Learning Models and the Hybrid Similarity Search Algorithm

High-resolution LC-MS/MS tandem mass spectra-based machine learning models are constructed to address the analytical challenge of identifying unknown controlled substances and new psychoactive substances (NPS's). Using a training set comprised of 770 LC-MS/MS barcode spectra (with binary entries 0 or 1) obtained generally by high-resolution mass spectrometers, three classification machine learning models were generated and evaluated. The three models are artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbor (k-NN) models. In these models, controlled substances and NPS's were classified into 13 subgroups (benzylpiperazine, opiate, benzodiazepine, amphetamine, cocaine, methcathinone, classical cannabinoid, fentanyl, 2C series, indazole carbonyl compound, indole carbonyl compound, phencyclidine, and others). Using 193 LC-MS/MS barcode spectra as an external test set, accuracy of the ANN, SVM, and k-NN models were evaluated as 72.5%, 90.0%, and 94.3%, respectively. Also, the hybrid similarity search (HSS) algorithm was evaluated to examine whether this algorithm can successfully identify unknown controlled substances and NPS's whose data are unavailable in the database. When only 24 representative LC-MS/MS spectra of controlled substances and NPS's were selectively included in the database, it was found that HSS can successfully identify compounds with high reliability. The machine learning models and HSS algorithms are incorporated into our home-coded AI-SNPS (artificial intelligence screener for narcotic drugs and psychotropic substances) standalone software that is equipped with a graphic user interface. The use of this software allows unknown controlled substances and NPS's to be identified in a convenient manner.