Detecting semen stains on fabrics using near infrared hyperspectral images and multivariate models

Exploring stingless bee honey from selected regions of Peninsular Malaysia through gas chromatography-mass spectrometry-based untargeted metabolomics

Volatile organic compounds in honey are known for their considerable impact on the organoleptic properties of honey, such as aroma, flavor, taste, and texture. The type and composition of volatile organic compounds are influenced by entomological, geographical, and botanical origins; thus, these compounds have the potential to be chemical markers. Sixty-two volatile compounds were identified using gas chromatography-mass spectrometry from 30 Heterotrigona itama (H. itama) honey samples from 3 different geographical origins. Hydrocarbons and benzene derivatives were the dominant classes of volatile organic compounds in the samples. Both clustering and discriminant analyses demonstrated a clear separation between samples from distant origins (Kedah and Perak), and the volcano plot supported it. The reliability and predictability of the partial least squares-discriminant analysis model from the discriminant analysis were validated using cross-validation (R2 : 0.93; Q2 : 0.83; accuracy: 0.97) and the permutation test (p < 0.001), and the output depicted that the model is legitimate. In combination with the variable importance of projection (VIP > 1.0) and the Kruskal-Wallis test (p < 0.01), 19 volatile organic compounds (encompassed aldehydes, benzene derivatives, esters, hydrocarbons, and terpenoids) were sorted and named potent chemical markers in classifying honey samples from three geographical origins. In brief, this study illustrated that volatile organic compounds of stingless honey originated from the same bee species, but different geographical origins could be applied as chemical markers.

Estimation of time since deposition of semen stain on different fabric types using ATR-FTIR spectroscopy and chemometrics

Various body fluids such as blood, semen, vaginal secretions, and saliva are frequently encountered at crime scene. In cases of sexual assault, semen stains are one of the most reliable evidence of biological origin. In this study, our objective was to develop a method for estimating the time since deposition of semen stains on five different fabric types using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy, with a focus on a time frame of up to 8 weeks. Semen samples from six different volunteers were dripped onto five distinct fabric materials, and ATR-FTIR measurements were obtained at 17 different time points. Principal component analysis (PCA) and partial least squares (PLS) methods were employed to differentiate semen stains on various fabric samples and estimate the age of semen stains. Models constructed using PCA and PLSR achieved high R2 values and low root-mean-square error (RMSE). While the performance varies depending on fabric types, it was observed that age estimation of semen stains can be made within following intervals: 0.39-0.76 days for 0-7 day range, 2.59-3.38 days for the 1-8 week range, and 3.98-8.1 days for the 0-56 day range. This study demonstrates the effectiveness of using ATR-FTIR spectroscopy in combination with chemometrics to estimate the age of human semen stains on various fabric types based on time-dependent spectral changes.

Forensic analysis of biological fluid stains on substrates by spectroscopic approaches and chemometrics: A review

BACKGROUND

Bodily fluid stains are one of the most relevant evidence that can be found at the crime scene as it provides a wealth of information to the investigators. They help to report on the individuals involved in the crime, to check alibis, or to determine the type of crime that has been committed. They appear as stains in different types of substrates, some of them porous, which can interfere in the analysis. The spectroscopy techniques combined with chemometrics are showing increasing potential for their use in the analysis of such samples due to them being fast, sensitive, and non-destructive.

FINDINGS

This is a comprehensive review of the studies that used different spectroscopic techniques followed by chemometrics for analysing biological fluid stains on several surfaces, and under various conditions. It focuses on the bodily fluid stains and the most suitable spectroscopic techniques to study forensic scientific problems such as the substrate's characteristics, the influence of ambient conditions, the aging process of the bodily fluids, the presence of animal bodily fluids and non-biological fluids (interfering substances), and the bodily fluid mixtures. The most widely used techniques were Raman spectroscopy and attenuated total reflection Fourier transform infrared spectroscopy (ATR FTIR). Nonetheless, other non-destructive techniques have been also used, like near infrared hyperspectral imaging (HSI-NIR) or surface enhanced Raman spectroscopy (SERS), among others. This work provides the criteria for the selection of the most promising non-destructive techniques for the effective in situ detection of biological fluid stains at crime scene investigations.

SIGNIFICANCE AND NOVELTY

The use of the proper spectroscopic and chemometric approaches on the crime scene is expected to improve the support of forensic sciences to criminal investigations. Evidence may be analysed in a non-destructive manner and kept intact for further analysis. They will also speed up forensic investigations by allowing the selection of relevant samples from occupational ones.

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.

Long time stability of 35 small endogenous biomolecules in dried urine spotted on various surfaces and environmental conditions

Analysis of endogenous biomolecules is an important aspect of many forensic investigations especially with focus on DNA analysis for perpetrator/victim identification and protein analysis for body fluid identification. Recently, small endogenous biomolecules have been used for differentiation of synthetic "fake" urine from authentic urine and might be also useful for biofluid identification. Therefore, the aim of this study was to adapt and optimize a method for analysis of small EBs and to investigate long time stability of 35 small endogenous biomolecules (including acylcarnitines with their isomers and metabolites as well as amino acids with their metabolites) in spotted urine samples. Urine samples were spotted on seven different surfaces (Whatman 903 Protein Saver Cards, cotton swabs, cotton glove, denim, underwear, and smooth and rough flagstone) and stored under six environmental conditions (reference condition, sunlight, LED light, 4 °C, 37 °C, humidity of 95%). At certain time points (d0, d7, d28 and d56) samples were analyzed in triplicates by an optimized extraction and LC-HRMS approach. In addition, the urine marker Tamm-Horsfall-Protein was determined on cotton swabs at the same time points using a commercial lateral flow test. Twenty-one of 35 small endogenous biomolecules were stable on most materials/surfaces and under most storage conditions. Significant lower endogenous biomolecule peak areas were found for rough flagstone and underwear as well as for high humidity storage. Kynurenic acid proved to be photo labile. While high long time stabilities were found for 19 of 28 acylcarnitines, nine acylcarnitines showed aberrant stability patterns without evident structural reason. For Tamm-Horsfall-Protein degradation within 28 days was observed even under reference conditions. The presented study demonstrated the value of sensitive LC-HRMS analysis for small endogenous biomolecules / pattern. However, further studies will be indispensable for unambiguous body fluid identification by small endogenous biomolecules.

Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding

Hyperspectral imaging has recently gained increasing attention from academic and industrial world due to its capability of providing both spatial and physico-chemical information about the investigated objects. While this analytical approach is experiencing a substantial success and diffusion in very disparate scenarios, far less exploited is the possibility of collecting sequences of hyperspectral images over time for monitoring dynamic scenes. This trend is mainly justified by the fact that these so-called hyperspectral videos usually result in BIG DATA sets, requiring TBs of computer memory to be both stored and processed. Clearly, standard chemometric techniques do need to be somehow adapted or expanded to be capable of dealing with such massive amounts of information. In addition, hyperspectral video data are often affected by many different sources of variations in sample chemistry (for example, light absorption effects) and sample physics (light scattering effects) as well as by systematic errors (associated, e.g., to fluctuations in the behaviour of the light source and/or of the camera). Therefore, identifying, disentangling and interpreting all these distinct sources of information represents undoubtedly a challenging task. In view of all these aspects, the present work describes a multivariate hybrid modelling framework for the analysis of hyperspectral videos, which involves spatial, spectral and temporal parametrisations of both known and unknown chemical and physical phenomena underlying complex real-world systems. Such a framework encompasses three different computational steps: 1) motions ongoing within the inspected scene are estimated by optical flow analysis and compensated through IDLE modelling; 2) chemical variations are quantified and separated from physical variations by means of Extended Multiplicative Signal Correction (EMSC); 3) the resulting light scattering and light absorption data are subjected to the On-The-Fly Processing and summarised spectrally, spatially and over time. The developed methodology was here tested on a near-infrared hyperspectral video of a piece of wood undergoing drying. It led to a significant reduction of the size of the original measurements recorded and, at the same time, provided valuable information about systematic variations generated by the phenomena behind the monitored process.

A novel proposal to investigate the interplay between the spatial and spectral domains in near-infrared spectral imaging data by means of Image Decomposition, Encoding and Localization (IDEL)

The emergence of new spectral imaging applications in many science fields and in industry has not come to be a surprise, considering the immense potential this technique has to map spectral information. In the case of near-infrared spectral imaging, a rapid evolution of the technology has made it more and more appealing in non-destructive analysis of food and materials as well as in process monitoring applications. However, despite its great diffusion, some challenges remain open from the data analysis point of view, with the aim to fully uncover patterns and unveil the interplay between both the spatial and spectral domains. Here we propose a new approach, called Image Decomposition, Encoding and Localization (IDEL), where a spatial perspective is taken for the analysis of spectral images, while maintaining the significant information within the spectral domain. The methodology benefits from wavelet transform to exploit spatial features, encoding the outcoming images into a set of descriptors and utilizing multivariate analysis to isolate and extract the significant spatial-spectral information. A forensic case study of near-infrared images of biological stains on cotton fabrics is used as a benchmark. The stain and fabric have hardly distinguishable spectral signatures due to strong scattering effects that originate from the rough surface of the fabric and the high spectral absorbance of cotton in the near-infrared range. There is no selective information that can isolate signals related to these two components in the spectral images under study, and the complex spatial structure is highly interconnected to the spectral signatures. IDEL was capable of isolating the stains, (spatial) scattering effects, and a possible drying effect from the stains. It was possible to recover, at the same time, specific spectral regions that mostly highlight these isolated spatial structures, which was previously unobtainable.

Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study

Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A faster and cheaper methodology based on automatic methods can be useful for the detection and identification of Cannabis sativa L. in a reliable and objective manner. In this work, the high potential of Near Infrared Hyperspectral Imaging (HSI-NIR) combined with machine learning is demonstrated for supervised detection and classification of Cannabis sativa L. This plant, together with other plants commonly found in the surroundings of illegal plantations and soil, were directly collected from an illegal plantation. Due to the high correlation of the NIR spectra, sparse Principal Component Analysis (sPCA) was implemented to select the most important wavelengths for identifying Cannabis sativa L. One class Soft Independent Class Analogy model (SIMCA) was built, considering just the spectral variables selected by sPCA. Sensitivity and specificity values of 89.45% and 97.60% were, respectively, obtained for an external validation set subjected to the s-SIMCA. The results proved the reliability of a methodology based on NIR hyperspectral cameras to detect and identify Cannabis sativa L., with only four spectral bands, showing the potential of this methodology to be implemented in low-cost airborne devices.

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.

Detection and discrimination of seminal fluid using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy combined with chemometrics

Semen is most frequently encountered body fluid in forensic cases apart from blood especially in sexual assault cases. The presence and absence of semen can help in conviction or exoneration of a suspect by either confirming or refuting the claims put forward by the suspect and the victim. However, in the wake of limited studies on non-destructive and rapid analysis of semen, it is fairly difficult. Therefore, it is an increasing demand to pioneer the application of available analytical methods in such manner that non-destructive, automated, rapid, and reliable identification and discrimination of body fluids can be established. In the present study, such a methodological application of attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy has been put forward as one of the initial steps towards the identification and discrimination/classification of seminal fluid from vaginal fluid and other human biological as well as non-biological look-alike semen substances using chemometric tools which are principal component analysis (PCA), partial least square regression (PLSR), and linear discriminant analysis (LDA). Effect of other simulated factors such as substrate interference, mixing with other body fluids, dilutions, and washing and chemical treatments to the samples has been studied. PCA resulted in 98.8% of accuracy for the discrimination of seminal fluid from vaginal fluid whilst 100% accuracy was obtained using LDA method. One hundred percent discrimination was achieved to discriminate semen from other biological fluids using PLSR and LDA, and from non-biological substances using PCA-LDA models. Furthermore, results of the effect of substrates, chemical treatment, mixing with vaginal secretions, and dilution have also been described.

Reflective spectroscopy investigations of clothing items to support law enforcement, search and rescue, and war crime investigations

Clothing articles are important pieces of evidence in criminal, search and rescue, and search and recovery investigations. Hyperspectral remote sensing of clothing will be an important tool for supporting such investigations in the near future. This study investigated over 300 items of clothing that varied in fabric type, texture, color, and pattern. Clothing items were analyzed using an ASD FieldSpec 4 High Resolution spectroradiometer with a contact probe attachment. Of the clothing items analyzed, there were 141 having endmember fabrics (100% single fabric type composition): 89 were cotton, 39 were polyester, 5 were wool, 1 was cashmere, 3 were acrylic, 1 was leather, and 3 were rayon. The remaining 164 clothing items were various fabric blends. Spectral features relating to different fabric types exhibit sufficient differences that allow them to be discriminated from the surrounding environment, as well as from one another in many, but not all, cases. Cotton and polyester, in particular, two of the most widely-used fabrics, exhibit numerous features in the near infrared (NIR) and shortwave infrared (SWIR) that would allow them to easily be distinguished from geologic materials in the environment such as rocks and soil. Plant based fibers, especially cotton, possess similar reflectance features to vegetation owing to their cellulose content. Outdoor aging experiments were conducted for 19 weeks on selected fabrics. Although significant changes were observed in aged garments, the variability observed in the reflectance of the aged garments does not support the derivation of a metric for aging, at least over the relatively short time scale of this effort. Results from this study should support numerous forensic efforts globally for non-destructive investigation of clothing items in the field and in lab settings with a spectroradiometer, enhance the potential for remote sensing searches, and in the future, potentially documenting crime scenes with hyperspectral imaging.

Estimation of the age of human semen stains by attenuated total reflection Fourier transform infrared spectroscopy: a preliminary study

Semen stain is one of the most important biological evidence at sexual crime scenes. Age estimation of human semen stains plays an important role in forensic work, and it is rarely studied due to lack of well-established methods. In this study, the technique called attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) coupled with advanced chemometric methods was employed to determine the age of semen stains on three different substrates: glass slides, tissues and fabric made of regenerated cellulose fibres up to 6 d. Partial least squares regression (PLSR) was used in conjunction with spectral analysis for age estimation, and the results generated high R² values (cross-validation: 0.81, external validation: 0.74) but a narrow margin of error for root mean square error (RMSE) (RMSE of cross-validation: 0.77 d, RMSE of prediction: 1.02 d). Additionally, our results indicated the robustness of PLSR model was not weaken by the influence of different substrates in this study. Our results indicate that ATR-FTIR, combined with chemometric methods, shows great potential as a convenient and efficient tool for age estimation of semen stains. Moreover, the method could be applied to routine forensic investigations in the future.

Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System

The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400–1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives.