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

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.

Vibrational Spectroscopy in Urine Samples as a Medical Tool: Review and Overview on the Current State-of-the-Art

Although known since the first half of the twentieth century, the evolution of spectroscopic techniques has undergone a strong acceleration after the 2000s, driven by the successful development of new computer technologies suitable for analyzing the large amount of data obtained. Today's applications are no longer limited to analytical chemistry, but are becoming useful instruments in the medical field. Their versatility, rapidity, the volume of information obtained, especially when applied to biological fluids that are easy to collect, such as urine, could provide a novel diagnostic tool with great potential in the early detection of different diseases. This review aims to summarize the existing literature regarding spectroscopy analyses of urine samples, providing insight into potential future applications.

Urinalysis of individuals with renal hyperfiltration using ATR-FTIR spectroscopy

Abnormal increased glomerular filtration rate (GFR), otherwise known as renal hyperfiltration (RHf), is associated with an increased risk of chronic kidney disease and cardiovascular mortality. Although it is not considered as a disease alone in medicine today, early detection of RHf is essential to reducing risk in a timely manner. However, detecting RHf is a challenge since it does not have a practical biochemical marker that can be followed or quantified. In this study, we tested the ability of ATR-FTIR spectroscopy to distinguish 17 individuals with RHf (hyperfiltraters; RHf (+)), from 20 who have normal GFR (normofiltraters; RHf(-)), using urine samples. Spectra collected from hyperfiltraters were significantly different from the control group at positions 1621, 1390, 1346, 933 and 783/cm. Intensity changes at these positions could be followed directly from the absorbance spectra without the need for pre-processing. They were tentatively attributed to urea, citrate, creatinine, phosphate groups, and uric acid, respectively. Using principal component analysis (PCA), major peaks of the second derivative forms for the classification of two groups were determined. Peaks at 1540, 1492, 1390, 1200, 1000 and 840/cm were significantly different between the two groups. Statistical analysis showed that the spectra of normofiltraters are similar; however, those of hyperfiltraters show diversity at multiple positions that can be observed both from the absorbance spectra and the second derivative profiles. This observation implies that RHf can simultaneously affect the excretion of many substances, and that a spectroscopic analysis of urine can be used as a rapid and non-invasive pre-screening tool.

Unlocking the potential of forensic traces: Analytical approaches to generate investigative leads

Forensic investigation involves gathering the information necessary to understand the criminal events as well as linking objects or individuals to an item, location or other individual(s) for investigative purposes. For years techniques such as presumptive chemical tests, DNA profiling or fingermark analysis have been of great value to this process. However, these techniques have their limitations, whether it is a lack of confidence in the results obtained due to cross-reactivity, subjectivity and low sensitivity; or because they are dependent on holding reference samples in a pre-existing database. There is currently a need to devise new ways to gather as much information as possible from a single trace, particularly from biological traces commonly encountered in forensic casework. This review outlines the most recent advancements in the forensic analysis of biological fluids, fingermarks and hair. Special emphasis is placed on analytical methods that can expand the information obtained from the trace beyond what is achieved in the usual practices. Special attention is paid to those methods that accurately determine the nature of the sample, as well as how long it has been at the crime scene, along with individualising information regarding the donor source of the trace.

Sex discrimination from urine traces for forensic purposes using attenuated total reflectance Fourier transform infrared spectroscopy and multivariate data analysis

The characteristics of ATR FT-IR spectroscopy are extremely attractive and escalating popularity in the field of body fluid analysis owing to its non-destructive, rapid, and reliable nature. Herein, the present study establishes that how ATR FT-IR spectroscopy could be utilized as a non-destructive, non-invasive, and confirmatory technique for sex discrimination from dry urine traces. Traces of body fluids are of paramount importance to criminal investigations as a major source of individualization by DNA profiling. However, the significance of DNA profiling from urine traces is highly diminished due to the small amount of DNA in urine traces. For that reason, the sex discrimination between the male and female donors is sorely desirable. In this study, ATR FT-IR spectroscopy in combination with partial least squares-discriminant analysis (PLS-DA) model unequivocally demonstrated the successful sex discrimination of an individual from dry traces of urine with 95.3% accuracy. PCA-Linear Discriminant Analysis (LDA) approach provided 85.2% of accuracy; however, PCA could not provide the sufficient findings for the discrimination of male and female urine spectra. The validation study was conducted and obtained 0% rates of false-positive and negative assignments. Additionally, this study also attended to assess the influence of substrates on the analysis of urine traces and results have been discussed.

Integrative measurement analysis via machine learning descriptor selection for investigating physical properties of biopolymers in hairs

Integrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional information measured by multiple analytical techniques, aiming to reveal their relationships with physical properties of biopolymers derived from hair. Hair samples were analyzed by multiple techniques, including solid-state nuclear magnetic resonance (NMR), time-domain NMR, Fourier transform infrared spectroscopy, and thermogravimetric and differential thermal analysis. The measured data were processed by different processing techniques, such as spectral differentiation and deconvolution, and then converted into a variety of “measurement descriptors” with different compositional information. The descriptors were associated with the mechanical properties of hair by constructing prediction models using machine learning algorithms. Herein, the stepwise model refinement via selection of adopted descriptors based on importance evaluation identified the most contributive descriptors, which provided an integrative interpretation about the compositional factors, such as α-helix keratins in cortex; and bounded water and thermal resistant components in cuticle. These results demonstrated the efficacy of the present strategy to generate and select descriptors from manifold measured data for investigating the nature of sophisticated subjects, such as hair.

Diagnosis of liver cancer by FTIR spectra of serum

Liver cancer is the most common fatal malignant tumor in the world. Early diagnosis of liver cancer can improve the survival rate of the patients with liver disease. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with curve fitting and chemometrics was used to distinguish the serum from patients from that of healthy people. The curve fitting results in protein range of 1700-1600 cm^-1 showed that there were differences in the secondary structure of protein in serum between the patients with liver cancer and healthy people. Principal component analysis (PCA) in lipid range of 2900-2800 cm^-1 could distinguish the serum of patients with liver cancer from that of healthy people. The first two principal components PC1 and PC2 explained 95% of the total data variance. The sensitivity and specificity of partial least squares discriminant analysis (PLS-DA) in lipid range of 2900-2800 cm^-1 reached 92.85% and 95.23% respectively. It is shown that FTIR spectroscopy might be developed as an effective method for the diagnosis of liver cancer.

Recent advances of vibrational spectroscopy and chemometrics for forensic biological analysis

Biological materials found at a crime scene are crucially important evidence for forensic investigation because they provide contextual information about a crime and can be linked to the donor-individuals through combination with DNA analysis. Applications of vibrational spectroscopy to forensic biological analysis have been emerging because of its advantageous characteristics such as the non-destructivity, rapid measurement, and quantitative evaluation, compared to most current methods based on histological observation or biochemical techniques. This review presents an overview of recent developments in vibrational spectroscopy for forensic biological analysis. We also emphasize chemometric techniques, which can elicit reliable and advanced analytical outputs from highly complex spectral data from forensic biological materials. The analytical subjects addressed herein include body fluids, hair, soft tissue, bones, and bioagents. Promising applications for various analytical purposes in forensic biology are presented. Simultaneously, future avenues of study requiring further investigation are discussed.

On the Identification of Body Fluids and Tissues: A Crucial Link in the Investigation and Solution of Crime

Body fluid and body tissue identification are important in forensic science as they can provide key evidence in a criminal investigation and may assist the court in reaching conclusions. Establishing a link between identifying the fluid or tissue and the DNA profile adds further weight to this evidence. Many forensic laboratories retain techniques for the identification of biological fluids that have been widely used for some time. More recently, many different biomarkers and technologies have been proposed for identification of body fluids and tissues of forensic relevance some of which are now used in forensic casework. Here, we summarize the role of body fluid/ tissue identification in the evaluation of forensic evidence, describe how such evidence is detected at the crime scene and in the laboratory, elaborate different technologies available to do this, and reflect real life experiences. We explain how, by including this information, crucial links can be made to aid in the investigation and solution of crime.

Urine analysis using FTIR spectroscopy: A study on healthy adults and children

Urine spectra from 108 healthy volunteers are studied by attenuated total refraction-Fourier transform infrared (ATR-FTIR) spectroscopy. The spectral features are correlated with observable urine components. The variation of spectra within a healthy population is quantified and a library of reference spectra is constructed. Using the band assignments, these spectra are compared with both age-wise and gender-wise. Children show the least intensity variations compared to both adult groups. Young adults show the highest variation, particularly in the 1650 to 1400 cm^-1 and 1200 to 900 cm^-1 regions. These results indicate the importance of the size of the control group in comparative studies utilizing FTIR. Age-wise comparisons reveal that phosphate and sulfate excretion decreases with age, and that the variance of phosphate among individuals is higher with adults. As for gender-wise comparisons, females show a slightly higher citrate content at 1390 cm^-1 regardless of the age and they show a higher variance in the 1200 to 1000 cm^-1 region when compared to men.

Functional Group Identification for FTIR Spectra Using Image-Based Machine Learning Models

Fourier transform infrared spectroscopy (FTIR) is a ubiquitous spectroscopic technique. Spectral interpretation is a time-consuming process, but it yields important information about functional groups present in compounds and in complex substances. We develop a generalizable model via a machine learning (ML) algorithm using convolutional neural networks (CNNs) to identify the presence of functional groups in gas-phase FTIR spectra. The ML models reduce the amount of time required to analyze functional groups and facilitate interpretation of FTIR spectra. Through web scraping, we acquire intensity-frequency data from 8728 gas-phase organic molecules within the NIST spectral database and transform the data into spectral images. We successfully train models for 15 of the most common organic functional groups, which we then determine via identification from previously untrained spectra. These models serve to expand the application of FTIR measurements for facile analysis of organic samples. Our approach was done such that we have broad functional group models that infer in tandem to provide full interpretation of a spectrum. We present the first implementation of ML using image-based CNNs for predicting functional groups from a spectroscopic method.

Analysis of the ex-vivo transformation of semen, saliva and urine as they dry out using ATR-FTIR spectroscopy and chemometric approach

The ex-vivo biochemical changes of different body fluids also referred as aging of fluids are potential marker for the estimation of Time since deposition. Infrared spectroscopy has great potential to reveal the biochemical changes in these fluids as previously reported by several researchers. The present study is focused to analyze the spectral changes in the ATR-FTIR spectra of three body fluids, commonly encountered in violent crimes i.e., semen, saliva, and urine as they dry out. The whole analytical timeline is divided into relatively slow phase I due to the major contribution of water and faster Phase II due to significant evaporation of water. Two spectral regions i.e., 3200–3400 cm⁻¹ and 1600–1000 cm⁻¹ are the major contributors to the spectra of these fluids. Several peaks in the spectral region between 1600 and 1000 cm⁻¹ showed highly significant regression equation with a higher coefficient of determination values in Phase II in contrary to the slow passing Phase I. Principal component and Partial Least Square Regression analysis are the two chemometric tool used to estimate the time since deposition of the aforesaid fluids as they dry out. Additionally, this study potentially estimates the time since deposition of an offense from the aging of the body fluids at the early stages after its occurrence as well as works as the precursor for further studies on an extended timeframe.

Discrimination of menstrual and peripheral blood traces using attenuated total reflection Fourier transform-infrared (ATR FT-IR) spectroscopy and chemometrics for forensic purposes

Body fluid traces can provide highly valuable clues in forensic investigations. In particular, bloodstains are a common occurrence in criminal investigation, and the discrimination of menstrual and peripheral blood is a crucial step for casework involving rape and sexual assault. Most of the current protocols require the detection of characteristic menstrual blood components using sophisticated procedures that need to be performed in a laboratory. The present study uses attenuated total reflection Fourier transform-infrared (ATR FT-IR) spectroscopy as a nondestructive technique for discriminating menstrual and peripheral blood traces. This method incorporates statistical analysis and was evaluated by internal and external validation testing. A partial least squares discriminant analysis (PLSDA) classification model was created for differentiating the two types of blood in a binary manner. Excellent separation between menstrual and peripheral blood samples was achieved during internal validation. External validation resulted in 100% accuracy for predicting a sample as peripheral or menstrual blood. This study demonstrates that ATR FT-IR spectroscopy combined with chemometrics is a reliable approach for rapid and nondestructive discrimination of menstrual and peripheral bloodstains. It offers a significant advantage to forensic science due to the availability of portable instruments and the potential for bloodstain analysis at a crime scene. Graphical abstract.

Polarimetric Balanced Detection: Background-Free Mid-IR Evanescent Field Laser Spectroscopy for Low-Noise, Long-term Stable Chemical Sensing

In this work, we introduce polarimetric balanced detection as a new attenuated total reflection (ATR) infrared (IR) sensing scheme, leveraging unequal effective thicknesses achieved with laser light of different polarizations. We combined a monolithic widely tunable Vernier quantum cascade laser (QCL-XT) and a multibounce ATR IR spectroscopy setup for analysis of liquids in a process analytical setting. Polarimetric balanced detection enables simultaneous recording of background and sample spectra, significantly reducing long-term drifts. The root-mean-square noise could be improved by a factor of 10 in a long-term experiment, compared to conventional absorbance measurements obtained via the single-ended optical channel. The sensing performance of the device was further evaluated by on-site measurements of ethanol in water, leading to an improved limit of detection (LOD) achieved with polarimetric balanced detection. Sequential injection analysis was employed for automated injection of samples into a custom-built ATR flow cell mounted above a zinc sulfide multibounce ATR element. The QCL-XT posed to be suitable for mid-IR-based sensing in liquids due to its wide tunability. Polarimetric balanced detection proved to enhance the robustness and long-term stability of the sensing device, along with improving the LOD by a factor of 5. This demonstrates the potential for new polarimetric QCL-based ATR mid-IR sensing schemes for in-field measurements or process monitoring usually prone to a multitude of interferences.

Forensic Phenotype Profiling Based on the Attenuated Total Reflection Fourier Transform-Infrared Spectroscopy of Blood: Chronological Age of the Donor

Forensic chemistry is an important and rapidly growing branch of analytical chemistry. As a part of forensic practices, phenotype profiling is beneficial to help narrow down suspects. The goal of this study is to identify a person's age range using dried bloodstains. Attenuated total reflection Fourier transform-infrared (ATR FT-IR) spectroscopy is the technique used to acquire information about the total (bio)chemical composition of a sample. For the purpose of this proof-of-concept study, a diverse pool of donors including those in newborn (<1), adolescent (11-13), and adult (43-68) age ranges was used. Different donor age groups were found to have different levels of lipids, glucose, and proteins in whole blood, although the corresponding spectral differences were minor. Therefore, the collected data set was analyzed using chemometrics to enhance discrepancy and assist in donors' classification. A partial least squares discriminant analysis (PLSDA) was used to classify ATR FT-IR spectra of blood from newborn, adolescent, and adult donors. The method showed a 92% correct classification of spectra in leave-one-out cross-validation (LOOCV) of the model. Overall, ATR FT-IR spectroscopy is nondestructive and can be an infield method that can be used for a variety of forensic applications. In general, the developed approach combining ATR FT-IR spectroscopy and advanced statistics shows the great potential for classifying (bio)chemical samples exhibiting significant intra-class variations.

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.

Postmortem Diagnosis of Fatal Hypothermia by Fourier Transform Infrared Spectroscopic Analysis of Edema Fluid in Formalin-Fixed, Paraffin-Embedded Lung Tissues

The goal of this study was to investigate whether pulmonary edema could become a specific diagnostic marker for fatal hypothermia using Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics. The spectral profile analysis indicated that hypothermia fatalities associated with pulmonary edema fluid contained more β-sheet protein conformational structures than the control causes of death, which included sudden cardiac death, brain injury, cerebrovascular disease, mechanical asphyxiation, intoxication, and drowning. Subsequently, the results of principal component analysis (PCA) further revealed that the content of β-sheet protein conformational structures in the pulmonary edema fluid was the main discriminatory marker between fatal hypothermia and the other causes of death. Ultimately, a robust postmortem diagnostic model for fatal hypothermia using a partial least-squares discriminant analysis (PLS-DA) algorithm was constructed. Pulmonary edema fluid spectra collected from eight new forensic autopsy cases that did not participate in the construction of the diagnostic model were predicted using the model. The results showed the causes of death of all these eight cases were correctly classified. In conclusion, this preliminary study demonstrates that FTIR spectroscopy in combination with chemometrics could be a promising approach for the postmortem diagnosis of fatal hypothermia.