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.

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.

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.

Forensics: evidence examination via Raman spectroscopy

Forensic science can be broadly defined as the application of any of the scientific method to solving a crime. Within forensic science there are many different disciplines, however, for the majority of them, five main concepts shape the nature of forensic examination: transfer, identification, classification/individualization, association, and reconstruction. The concepts of identification, classification/individualization, and association rely greatly on analytical chemistry techniques. It is, therefore, no stretch to see how one of the rising stars of analytical chemistry techniques, Raman spectroscopy, could be of use. Raman spectroscopy is known for needing a small amount of sample, being non-destructive, and very substance specific, all of which make it ideal for analyzing crime scene evidence. The purpose of this chapter is to show the state of new methods development for forensic applications based on Raman spectroscopy published between 2015 and 2017.

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.

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.

Enzymatic analysis of α-ketoglutaramate--a biomarker for hyperammonemia

Two enzymatic assays were developed for the analysis of α-ketoglutaramate (KGM)-an important biomarker of hepatic encephalopathy and other hyperammonemic diseases. In both procedures, KGM is first converted to α-ketoglutarate (KTG) via a reaction catalyzed by ω-amidase (AMD). In the first procedure, KTG generated in the AMD reaction initiates a biocatalytic cascade in which the concerted action of alanine transaminase and lactate dehydrogenase results in the oxidation of NADH. In the second procedure, KTG generated from KGM is reductively aminated, with the concomitant oxidation of NADH, in a reaction catalyzed by L-glutamic dehydrogenase. In both assays, the decrease in optical absorbance (λ=340 nm) corresponding to NADH oxidation is used to quantify concentrations of KGM. The two analytical procedures were applied to 50% (v/v) human serum diluted with aqueous solutions containing the assay components and spiked with concentrations of KGM estimated to be present in normal human plasma and in plasma from hyperammonemic patients. Since KTG is the product of AMD-catalyzed hydrolysis of KGM, in a separate study, this compound was used as a surrogate for KGM. Statistical analyses of samples mimicking the concentration of KGM assumed to be present in normal and pathological concentration ranges were performed. Both enzymatic assays for KGM were confirmed to discriminate between the predicted normal and pathophysiological concentrations of the analyte. The present study is the first step toward the development of a clinically useful probe for KGM analysis in biological fluids.

Analysis of biomarkers characteristic of porcine liver injury--from biomolecular logic gates to an animal model

A biocatalytic cascade for the analysis of the simultaneous increase in the concentration of two biomarkers characteristic of liver injury (alanine transaminase, ALT, and lactate dehydrogenase, LDH) was tested on real samples acquired from an animal model (domestic pigs, Sus scrofa domesticus) suffering from traumatic liver injury. A two-step reaction biocatalyzed in the presence of both enzyme-biomarkers resulted in the oxidation of NADH followed by optical absorbance measurements. A simple qualitative, YES/NO, test allowed for distinction between animals with and without the presence of liver injury with the probability of 92%. These data represent the first demonstration of applying binary logic systems for the analysis of real biomedical samples.

Biomolecular filters for improved separation of output signals in enzyme logic systems applied to biomedical analysis

Biomolecular logic systems processing biochemical input signals and producing "digital" outputs in the form of YES/NO were developed for analysis of physiological conditions characteristic of liver injury, soft tissue injury, and abdominal trauma. Injury biomarkers were used as input signals for activating the logic systems. Their normal physiological concentrations were defined as logic-0 level, while their pathologically elevated concentrations were defined as logic-1 values. Since the input concentrations applied as logic 0 and 1 values were not sufficiently different, the output signals being at low and high values (0, 1 outputs) were separated with a short gap making their discrimination difficult. Coupled enzymatic reactions functioning as a biomolecular signal processing system with a built-in filter property were developed. The filter process involves a partial back-conversion of the optical-output-signal-yielding product, but only at its low concentrations, thus allowing the proper discrimination between 0 and 1 output values.