Phenotypic profiling based on body fluid traces discovered at the scene of crime: Raman spectroscopy of urine stains for race differentiation.

Modern criminal investigations heavily rely on trace bodily fluid evidence as a rich source of DNA. DNA profiling of such evidence can result in the identification of an individual if a matching DNA profile is available. Alternatively, phenotypic profiling based on the analysis of body fluid traces can significantly narrow down the pool of suspects in a criminal investigation. Urine stain is a frequently encountered specimen at the scene of crime. Raman spectroscopy offers great potential as a universal confirmatory method for the identification of all main body fluids, including urine. In this proof-of-concept study, Raman spectroscopy combined with advanced statistics was used for race differentiation based on the analysis of urine stains. Specifically, a Random Forest (RF) model was built, which allowed for differentiating Caucasian (CA) and African American (AA) descent donors with 90% accuracy based on Raman spectra of dried urine samples. Raman spectra were collected from samples of 28 donors varying in age and sex. This novel technology offers great potential as a universal forensic tool for phenotypic profiling of a potential suspect immediately at the scene of a crime, providing invaluable information for a criminal investigation.

Metabolite monitoring concept for the biometric identification of individuals from the skin surface.

This study aims at proof of concept that constant monitoring of the concentrations of metabolites in three individuals' sweat over time can differentiate one from another at any given time, providing investigators and analysts with increased ability and means to individualize this bountiful biological sample. A technique was developed to collect and extract authentic sweat samples from three female volunteers for the analysis of lactate, urea, and L-alanine levels. These samples were collected 21 times over a 40-day period and quantified using a series of bioaffinity-based enzymatic assays with UV-vis spectrophotometric detection. Sweat samples were simultaneously dried, derivatized, and analyzed by a GC-MS technique for comparison. Both UV-vis and GC-MS analysis methods provided a statistically significant MANOVA result, demonstrating that the sum of the three metabolites could differentiate each individual at any given day of the time interval. Expanding upon previous studies, this experiment aims to establish a method of metabolite monitoring as opposed to single-point analyses for application to biometric identification from the skin surface.

Sex Determination of Human Nails Based on Attenuated Total Reflection Fourier Transform Infrared Spectroscopy in Forensic Context.

This study reports on the successful use of a machine learning approach using attenuated total reflectance Fourier transform infrared (ATR FT-IR) spectroscopy for the classification and prediction of a donor's sex from the fingernails of 63 individuals. A significant advantage of ATR FT-IR is its ability to provide a specific spectral signature for different samples based on their biochemical composition. The infrared spectrum reveals unique vibrational features of a sample based on the different absorption frequencies of the individual functional groups. This technique is fast, simple, non-destructive, and requires only small quantities of measured material with minimal-to-no sample preparation. However, advanced multivariate techniques are needed to elucidate multiplex spectral information and the small differences caused by donor characteristics. We developed an analytical method using ATR FT-IR spectroscopy advanced with machine learning (ML) based on 63 donors' fingernails (37 males, 26 females). The PLS-DA and ANN models were established, and their generalization abilities were compared. Here, the PLS scores from the PLS-DA model were used for an artificial neural network (ANN) to create a classification model. The proposed ANN model showed a greater potential for predictions, and it was validated against an independent dataset, which resulted in 92% correctly classified spectra. The results of the study are quite impressive, with 100% accuracy achieved in correctly classifying donors as either male or female at the donor level. Here, we underscore the potential of ML algorithms to leverage the selectivity of ATR FT-IR spectroscopy and produce predictions along with information about the level of certainty in a scientifically defensible manner. This proof-of-concept study demonstrates the value of ATR FT-IR spectroscopy as a forensic tool to discriminate between male and female donors, which is significant for forensic applications.

Attenuated Total Reflection Fourier Transform Infrared Spectroscopy for Forensic Screening of Long-Term Alcohol Consumption from Human Nails.

Fourier transform infrared (FT-IR) spectroscopy is used throughout forensic laboratories for many applications. FT-IR spectroscopy can be useful with ATR accessories in forensic analysis for several reasons. It provides excellent data quality combined with high reproducibility, with minimal user-induced variations and no sample preparation. Spectra from heterogeneous biological systems, including the integumentary system, can be associated with hundreds or thousands of biomolecules. The nail matrix of keratin possesses a complicated structure with captured circulating metabolites whose presence may vary in space and time depending on context and history. We developed a new approach by using machine-learning (ML) tools to leverage the potential and enhance the selectivity of the instrument, create classification models, and provide invaluable information saved in human nails with statistical confidence. Here, we report chemometric analysis of ATR FT-IR spectra for the classification and prediction of long-term alcohol consumption from nail clippings in 63 donors. A partial least squares discriminant analysis (PLS-DA) was used to create a classification model that was validated against an independent data set which resulted in 91% correctly classified spectra. However, when considering the prediction results at the donor level, 100% accuracy was achieved, and all donors were correctly classified. To the best of our knowledge, this proof-of-concept study demonstrates for the first time the ability of ATR FT-IR spectroscopy to discriminate donors who do not drink alcohol from those who drink alcohol on a regular basis.

Identification and highly selective differentiation of organic gunshot residues utilizing their elemental and molecular signatures.

Firearm related evidence is of great significance to forensic science. In recent years, many researchers have focused on exploring the probative value of organic gunshot residue (OGSR) evidence, which is often bolstered by many factors including recoverability. In addition, OGSR analysis has shown the potential to achieve differentiation between OGSRs generated from various ammunition brands and/or calibers. Raman spectroscopy is a vibrational spectroscopic technique which has been used in the past for gunshot residue analysis-including OGSR specifically. Raman spectroscopy is a nondestructive, highly-selective, simple, and rapid technique which provides molecular information about samples. LIBS or Laser-Induced Breakdown Spectroscopy is a simple, robust, and rapid analytical method which requires minimal to no sample preparation and a small amount of sample for analysis. LIBS provides information on the elemental compositions of samples. In this study, Raman spectroscopy and LIBS were used together in sequence in an attempt to achieve the specific identification and characterization of OGSR particles from ammunition types which were closely related. The main goal was to determine if this method had the potential to differentiate between various ammunition types of the same caliber and produced by the same manufacturer, and generated under identical firing conditions. High-resolution optical microscopy documented the OGSR particles' morphologies and Raman spectroscopy was used to identify particles as OGSRs. Finally, LIBS analysis of the OGSR particles was carried out. Advanced chemometric techniques were shown to allow for very successful differentiation between the OGSR samples analyzed.

Raman spectroscopy and machine learning for biomedical applications: Alzheimer's disease diagnosis based on the analysis of cerebrospinal fluid.

Current Alzheimer's disease (AD) diagnostics is based on clinical assessments, imaging and neuropsychological tests that are efficient only at advanced stages of the disease. Early diagnosis of AD will provide decisive opportunities for preventive treatment and development of disease-modifying drugs. Cerebrospinal fluid (CSF) is in direct contact with the human brain, where the deadly pathological process of the disease occurs. As such, the CSF biochemical composition reflects specific changes associated with the disease and is therefore the most promising body fluid for AD diagnostic test development. Here, we describe a new method to diagnose AD based on CSF via near infrared (NIR) Raman spectroscopy in combination with machine learning analysis. Raman spectroscopy is capable of probing the entire biochemical composition of a biological fluid at once. It has great potential to detect small changes specific to AD, even at the earliest stages of pathogenesis. NIR Raman spectra were measured of CSF samples acquired from 21 patients diagnosed with AD and 16 healthy control (HC) subjects. Artificial neural networks (ANN) and support vector machine discriminant analysis (SVM-DA) statistical methods were used for differentiation purposes, with the most successful results allowing for the differentiation of AD and HC subjects with 84% sensitivity and specificity. Our classification models show high discriminative power, suggesting the method has a great potential for AD diagnostics. The reported Raman spectroscopic examination of CSF can complement current clinical tests, making early AD detection fast, accurate, and inexpensive. While this study shows promise using a small sample set, further method validation on a larger scale is required to indicate the true strength of the approach.

Differentiating smokers and nonsmokers based on Raman spectroscopy of oral fluid and advanced statistics for forensic applications.

Raman spectroscopy has proven to be a valuable tool for analyzing various types of forensic evidence such as traces of body fluids. In this work, Raman spectroscopy was employed as a nondestructive technique for the analysis of dry traces of oral fluid to differentiate between smoker and nonsmoker donors with the aid of advanced statistical tools. A total of 32 oral fluid samples were collected from donors of differing gender, age and race and were subjected to Raman spectroscopic analysis. A genetic algorithm was used to determine eight spectral regions that contribute the most to the differentiation of smokers and nonsmokers. Thereafter, a classification model was developed based on the artificial neural network that showed 100% accuracy after external validation. The developed approach demonstrates great potential for the differentiation of smokers and nonsmokers based on the analysis of dry traces of oral fluid.

Noninvasive Concept for Optical Ethanol Sensing on the Skin Surface with Camera-Based Quantification.

Law enforcement and the general public do not yet have adequate means of assessing and preventing drunk driving. Blood alcohol concentration (BAC) is unable to be determined on-site, as it typically requires the use of complex chromatographic methods. Breathalyzers have been well established in law enforcement for correlating breath alcohol concentrations (BrAC) to BAC estimations, as they involve portable equipment with rapid analysis times. Although these BrAC measurements allow police officers to determine probable cause and to arrest an intoxicated driver at the scene, the results are preliminary and are not often considered as evidence in court. A new, noninvasive method was developed to assess an individual's level of intoxication based on the presence of ethanol in sweat on the skin surface. This intuitive system uses two enzymes, alcohol oxidase and horseradish peroxidase, to correlate ethanol sweat concentrations to the production of a color that is visible to the naked eye. The results of the controlled drinking study demonstrate the ability of both the spectrophotometric and the visualization system to quantify the amount of ethanol within authentic sweat samples collected from individuals who had consumed an alcoholic beverage. The pictorial analysis allows for the system to be analyzed without the use of a UV-vis spectrophotometer. With this method, a smartphone application would be capable of documenting and evaluating the intoxication levels of an individual based on sweat ethanol levels. The developed alcohol sensing system has the potential to impact both the general public and law enforcement, as well as the fields of forensic and biomedical science.

Screening for Alzheimer's Disease Using Saliva: A New Approach Based on Machine Learning and Raman Hyperspectroscopy.

BACKGROUND: Alzheimer's disease and related dementias (ADRDs) are being diagnosed at epidemic rates, with incidence to triple from 35 to 115 million cases worldwide. Most ADRDs are characterized by progressive neurodegeneration, and Alzheimer's disease (AD) is the sixth leading cause of death in the United States. The ideal moment for diagnosing ADRDs is during the earliest stages of its progression; however, current diagnostic methods are inefficient, expensive, and unsuccessful at making diagnoses during the earliest stages of the disease. OBJECTIVE: The aim of this project was to utilize Raman hyperspectroscopy in combination with machine learning to develop a novel method for the diagnosis of AD based on the analysis of saliva. METHODS: Raman hyperspectroscopy was used to analyze saliva samples collected from normative, AD, and mild cognitive impairment (MCI) individuals. Genetic Algorithm and Artificial Neural Networks machine learning techniques were applied to the spectral dataset to build a diagnostic algorithm. RESULTS: Internal cross-validation showed 99% accuracy for differentiating the three classes; blind external validation was conducted using an independent dataset to further verify the results, achieving 100% accuracy. CONCLUSION: Raman hyperspectroscopic analysis of saliva has a remarkable potential for use as a non-invasive, efficient, and accurate method for diagnosing AD.

Phenotype Profiling for Forensic Purposes: Determining Donor Sex Based on Fourier Transform Infrared Spectroscopy of Urine Traces.

Forensic science is an important field of analytical chemistry where vibrational spectroscopy, in particular Fourier transform infrared spectroscopy and Raman spectroscopy, present advantages as they have a nondestructive nature, high selectivity, and no need for sample preparation. Herein, we demonstrate a method for determination of donor sex, based on attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy of dry urine traces. Trace body fluid evidence is of special importance to the modern criminal investigation as a source of individualizing DNA evidence. However, individual identification of a urine donor is generally difficult because of the small amount of DNA. Therefore, the development of an innovative method to provide phenotype information about the urine donor-including sex-is highly desirable. In this study, we developed a multivariate discriminant model for the ATR FT-IR spectra of dry urine to identify the donor sex. Rigorous selection of significant wavenumbers on the spectrum using a genetic algorithm enabled superb discrimination performance for the model and conclusively indicated a chemical origin for donor sex differences, which was supported by physiological knowledge. Although further investigations need to be conducted, this proof-of-concept study demonstrates the great potential of the developed methodology for phenotype profiling based on the analysis of urine traces.

Raman microspectroscopic mapping as a tool for detection of gunshot residue on adhesive tape.

Our research group previously reported a novel method for the detection of gunshot residue (GSR) via tape lifting combined with Raman microspectroscopic mapping and multivariate analysis. This initial study achieved proof of concept for this approach. Here, we report validation studies which investigate the reproducibility/ruggedness and specificity of the approach. Raman mapping for GSR detection on adhesive tape was performed on an independent Raman microscope, not used to generate the training set. These independent spectra were classified against the original training dataset using support vector machine discriminant analysis (SVM-DA). The resulting classification rates of 100% illustrate the reproducibility of the technique, its independence upon a specific instrument and provide an external validation for the approach. Additionally, the same procedure for GSR collection (tape lifting) was performed to collect samples from environmental sources, which could potentially provide false-positive assignments for current GSR analysis techniques. Thus, particles associated with automotive mechanics were collected. Automotive brake and tire materials are often composed of the heavy metals lead, barium, and antimony, which are the key elements targeted by current GSR detection technique. It was determined that Raman spectroscopic analysis was not susceptible to misclassifications from these samples. Results from these validation experiments illustrate the great potential of Raman microspectroscopic mapping used with tape lifting as a viable complimentary tool to current methodologies for GSR detection. Furthermore, current methodologies are not well-developed for automated organic GSR detection. Illustrated here, Raman microscoptrosocpic mapping has the potential for the automatic identification of organic GSR. Graphical abstract ᅟ.

Surface enhanced Raman spectroscopy: A review of recent applications in forensic science.

Surface enhanced Raman spectroscopy has many advantages over its parent technique of Raman spectroscopy. Some of these advantages such as increased sensitivity and selectivity and therefore the possibility of small sample sizes and detection of small concentrations are invaluable in the field of forensics. A variety of new SERS surfaces and novel approaches are presented here on a wide range of forensically relevant topics.

Metabolite Biometrics for the Differentiation of Individuals.

Sweat is a biological fluid present on the skin surface of every individual and is known to contain amino acids as well as other low molecular weight compounds. (1) Each individual is inherently different from one another based on certain factors including, but not limited to, his/her genetic makeup, environment, and lifestyle. As such, the biochemical composition of each person greatly differs. The concentrations of the biochemical content within an individual's sweat are largely controlled by metabolic processes within the body that fluctuate regularly based on attributes such as age, sex, and activity level. Therefore, the concentrations of these sweat components are person-specific and can be exploited, as presented here, to differentiate individuals based on trace amounts of sweat. For this concept, we analyzed three model compounds-lactate, urea, and glutamate. The average absorbance change from each compound in sweat was determined using three separate bioaffinity-based systems: lactate oxidase coupled with horseradish peroxidase (LOx-HRP), urease coupled with glutamate dehydrogenase (UR-GlDH), and glutamate dehydrogenase alone (GlDH). After optimization of a linear dependence for each assay to its respective analyte, analysis was performed on 50 mimicked sweat samples. Additionally, a collection and extraction method was developed and optimized by our group to evaluate authentic sweat samples from the skin surface of 25 individuals. A multivariate analysis of variance (MANOVA) test was performed to demonstrate that these three single-analyte enzymatic assays were effectively used to identify each person in both sample sets. This novel sweat analysis approach is capable of differentiating individuals, without the use of DNA, based on the collective responses from the chosen metabolic compounds in sweat. Applications for this newly developed, noninvasive analysis can include the field of forensic science in order to differentiate between individuals as well as the fields of homeland security and cybersecurity for personal authentication via unlocking mechanisms in smart devices that monitor metabolites. Through further development and analysis, this concept also has the potential to be clinically applicable in monitoring the health of individuals based on particular biomarker combinations.

Fingerprint Analysis: Moving Toward Multiattribute Determination via Individual Markers.

Forensic science will be forever revolutionized if law enforcement can identify personal attributes of a person of interest solely from a fingerprint. For the past 2 years, the goal of our group has been to establish a way to identify originator attributes, specifically biological sex, from a single analyte. To date, an enzymatic assay and two chemical assays have been developed for the analysis of multiple analytes. In this manuscript, two additional assays have been developed. This time, however, the assays utilize only one amino acid each. The enzymatic assay targets alanine and employs alanine transaminase (ALT), pyruvate oxidase (POx), and horseradish peroxidase (HRP). The other, a chemical assay, is known as the Sakaguchi test and targets arginine. It is important to note that alanine has a significantly higher concentration than arginine in the fingerprint content of both males and females. Both assays proved to be capable of accurately differentiating between male and female fingerprints, regardless of their respective average concentration. The ability to target a single analyte will transform forensic science as each originator attribute can be correlated to a different analyte. This would then lead to the possibility of identifying multiple attributes from a single fingerprint sample. Ultimately, this would allow for a profile of a person of interest to be established without the need for time-consuming lab processes.

Coomassie Brilliant Blue G-250 Dye: An Application for Forensic Fingerprint Analysis.

The Bradford reagent, comprised of the Coomassie Brilliant Blue G-250 dye, methanol, and phosphoric acid, has been traditionally used for quantifying proteins. Use of this reagent in the Bradford assay relies on the binding of the Coomassie Blue G-250 dye to proteins. However, the ability of the dye to react with a small group of amino acids (arginine, histidine, lysine, phenylalanine, tyrosine, and tryptophan) makes it a viable chemical assay for fingerprint analysis in order to identify the biological sex of the fingerprint originator. It is recognized that the identification of biological sex has been readily accomplished using two other methods; however, both of those systems are reliant upon a large group of amino acids, 23 to be precise. The Bradford assay, described here, was developed specifically to aid in the transition from targeting large groups of amino acids, as demonstrated in the previous studies, to targeting only a single amino acid without compromising the intensity of the response and/or the ability to differentiate between two attributes. In this work, we aim to differentiate between female fingerprints and male fingerprints.

Race Differentiation by Raman Spectroscopy of a Bloodstain for Forensic Purposes.

Bearing in mind forensic purposes, a nondestructive and rapid method was developed for race differentiation of peripheral blood donors. Blood is an extremely valuable form of evidence in forensic investigations so proper analysis is critical. Because potentially miniscule amounts of blood traces can be found at a crime scene, having a method that is nondestructive, and provides a substantial amount of information about the sample, is ideal. In this study Raman spectroscopy was applied with advanced statistical analysis to discriminate between Caucasian (CA) and African American (AA) donors based on dried peripheral blood traces. Spectra were collected from 20 donors varying in gender and age. Support vector machines-discriminant analysis (SVM-DA) was used for differentiation of the two races. An outer loop subject-wise cross-validation (CV) method evaluated the performance of the SVM classifier for each individual donor from the training data set. The performance of SVM-DA, evaluated by the area under the curve (AUC) metric, showed 83% probability of correct classification for both races, and a specificity and sensitivity of 80%. This preliminary study shows promise for distinguishing between different races of human blood. The method has great potential for real crime scene investigation, providing rapid and reliable results, with no sample preparation, destruction, or consumption.

Ages at a Crime Scene: Simultaneous Estimation of the Time since Deposition and Age of Its Originator.

Blood is a major contributor of evidence in investigations involving violent crimes because of the unique composition of proteins and low molecular weight compounds present in the circulatory system, which often serve as biomarkers in clinical diagnostics. It was recently shown that biomarkers present in blood can also identify characteristics of the originator, such as ethnicity and biological sex. A biocatalytic assay for on-site forensic investigations was developed to simultaneously identify the age range of the blood sample originator and the time since deposition (TSD) of the blood spot. For these two characteristics to be identified, the levels of alkaline phosphatase (ALP), a marker commonly used in clinical diagnostics corresponding to old and young originators, were monitored after deposition for up to 48 h to mimic a crime scene setting. ALP was chosen as the biomarker due to its age-dependent nature. The biocatalytic assay was used to determine the age range of the originator using human serum samples. By means of statistical tools for evaluation and the physiological levels of ALP in healthy people, the applicability of this assay in forensic science was shown for the simultaneous determination of the age of the originator and the TSD of the blood spot. The stability of ALP in serum allows for the differentiation between old and young originators up to 2 days after the sample was left under mimicked crime scene conditions.

New Horizons for Ninhydrin: Colorimetric Determination of Gender from Fingerprints.

In the past century, forensic investigators have universally accepted fingerprinting as a reliable identification method via pictorial comparison. One of the most traditional detection methods uses ninhydrin, a chemical that reacts with amino acids in the fingerprint content to produce the blue-purple color known as Ruhemann's purple. It has recently been demonstrated that the amino acid content in fingerprints can be used to differentiate between male and female fingerprints. Here, we present a modified approach to the traditional ninhydrin method. This new approach for using ninhydrin is combined with an optimized extraction protocol and the concept of determining gender from fingerprints. In doing so, we are able to focus on the biochemical material rather than exclusively the physical image.

Forensic Identification of Gender from Fingerprints.

In the past century, forensic investigators have universally accepted fingerprinting as a reliable identification method, which relies mainly on pictorial comparisons. Despite developments to software systems in order to increase the probability and speed of identification, there has been limited success in the efforts that have been made to move away from the discipline's absolute dependence on the existence of a prerecorded matching fingerprint. Here, we have revealed that an information-rich latent fingerprint has not been used to its full potential. In our approach, the content present in the sweat left behind-namely the amino acids-can be used to determine physical such as gender of the originator. As a result, we were able to focus on the biochemical content in the fingerprint using a biocatalytic assay, coupled with a specially designed extraction protocol, for determining gender rather than focusing solely on the physical image.