Determining Gender by Raman Spectroscopy of a Bloodstain

Chick sexing based on the blood analysis using Raman spectroscopy

Efforts are underway to develop technology for automatically determining the sex of chick embryos, aimed at establishing a stable and efficient poultry farming system while also addressing animal welfare concerns. This study investigated the possibility of chick sexing through blood analysis using Raman spectroscopy. Raman spectra were obtained from whole blood and its constituents, such as red blood cells (RBCs) and blood plasma, collected from chicks aged 1-2 days, using a 785-nm excitation wavelength. Principal component analysis (PCA) revealed statistically significant sex-dependent spectral variations in whole blood and RBCs, whereas blood plasma showed less clear dependency. These spectral differences between male and female chicks were attributed to differences in the proportion of spectral components from oxygenated (oxy-) and deoxygenated (deoxy-) RBCs, with males exhibiting a slightly stronger contribution of oxy-RBCs compared to females. This reflects the higher oxygen affinity of hemoglobin (Hb) in males compared to females. A model for discriminating chick sex was built using the ratios of certain Raman band characteristics of oxy-RBCs and deoxy-RBCs, achieving a sensitivity of 100%. This spectroscopic method holds promise for developing technology to discriminate the sex of early chicken embryos in ovo by detecting differences in oxygen saturation of RBCs based on sex.

Differences in whole blood before and after hemodialysis session of subjects with chronic kidney disease measured by Raman spectroscopy

This study aimed to identify differences in the composition of whole blood of patients with chronic kidney disease (CKD), before and after a hemodialysis session (HDS), and possible differences in blood composition between stages and between genders using Raman spectroscopy and principal component analysis (PCA). Whole blood samples were collected from 40 patients (20 women and 20 men), before and after a HDS. Raman spectra were obtained and the spectra were evaluated by PCA and partial least squares (PLS) regression. Mean spectra and difference spectrum between the groups were calculated: stages Before and After HDS, and gender Women and Men, which had their most intense peaks identified. Stage: mean spectra and difference spectrum indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin in the group Before HDS. There was no statistically significant difference by PCA. Gender: mean spectra and difference spectrum Before HDS indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin with greater intensity in the group Women, and negative peaks to white blood cells and serum, with greater intensity in the group Men. There was statistically significant difference by PCA, which also identified the peaks assigned to white blood cells, serum and porphyrin for Women and red blood cells and amino acids (tryptophan) for Men. PLS model was able to classify the spectra of the gender with 83.7% accuracy considering the classification per patient. The Raman technique highlighted gender differences in pacients with CKD.

An integrated computational pipeline for machine learning-driven diagnosis based on Raman spectra of saliva samples

Raman Spectroscopy promises the ability to encode in spectral data the significant differences between biological samples belonging to patients affected by a disease and samples of healthy patients (controls). However, the decoding and interpretation of the Raman spectral fingerprint is still a difficult and time-consuming procedure even for domain experts. In this work, we test an end-to-end deep-learning diagnostic pipeline able to classify spectral data from saliva samples. The pipeline has been validated against the SARS-COV-2 Infection and for the screening of neurodegenerative diseases such as Parkinson's and Alzheimer's diseases. The proposed system can be used for the fast prototyping of promising non-invasive, cost and time-efficient diagnostic screening tests.

Use of Raman spectroscopy to study rat lung tissues for distinguishing asphyxia from sudden cardiac death

Determining asphyxia as the cause of death is crucial but is based on an exclusive strategy because it lacks sensitive and specific morphological characteristics in forensic practice. In some cases where the deceased has underlying heart disease, differentiation between asphyxia and sudden cardiac death (SCD) as the primary cause of death can be challenging. Herein, Raman spectroscopy was employed to detect pulmonary biochemical differences to discriminate asphyxia from SCD in rat models. Thirty-two rats were used to build asphyxia and SCD models, with lung samples collected immediately or 24 h after death. Twenty Raman spectra were collected for each lung sample, and 640 spectra were obtained for further data preprocessing and analysis. The results showed that different biochemical alterations existed in the lung tissues of the rats that died from asphyxia and SCD and could be used to distinguish between the two causes of death. Moreover, we screened and used 8 of the 11 main differential spectral features that maintained their significant differences at 24 h after death to successfully determine the cause of death, even with decomposition and autolysis. Eventually, seven prevalent machine learning classification algorithms were employed to establish classification models, among which the support vector machine exhibited the best performance, with an area under the curve value of 0.9851 in external validation. This study shows the promise of Raman spectroscopy combined with machine learning algorithms to investigate differential biochemical alterations originating from different deaths to aid determining the cause of death in forensic practice.

[Vibrational spectroscopy use for forensic purposes combined with machine learning]

Vibrational spectroscopy combined with machine learning has a great potential for forensic research. Portable Raman spectrometers are already being used by law-enforcement agencies to identify drugs. Several new technologies based on vibrational spectroscopy, that can be used in forensic science to analyze documents, gunshot traces, cloths, soil, hair, nails and lacquer, are being developed nowadays. The article considers the use of vibrational spectroscopy in forensic practice for conducting serological studies with an emphasis on the development of a universal method of identifying the main secretions of the body. The method allows to determine the time elapsed since the trace was made, as well as the phenotypic profile of host, including sex, race and age.

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.

A novel Raman spectroscopic method for detecting traces of blood on an interfering substrate

Traces of body fluids discovered at a crime scene are a primary source of DNA evidence. Raman spectroscopy is a promising universal technique for identifying biological stains for forensic purposes. The advantages of this method include the ability to work with trace amounts, high chemical specificity, no need for sample preparation and the nondestructive nature. However, common substrate interference limits the practical application of this novel technology. To overcome this limitation, two approaches called "Reducing a spectrum complexity" (RSC) and "Multivariate curve resolution combined with the additions method" (MCRAD) were investigated for detecting bloodstains on several common substrates. In the latter approach, the experimental spectra were "titrated" numerically with a known spectrum of a targeted component. The advantages and disadvantages of both methods for practical forensics were evaluated. In addition, a hierarchical approach to reduce the possibility of false positives was suggested.

BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference

The time since deposition (TSD) of a bloodstain, i.e., the time of a bloodstain formation is an essential piece of biological evidence in crime scene investigation. The practical usage of some existing microscopic methods (e.g., spectroscopy or RNA analysis technology) is limited, as their performance strongly relies on high-end instrumentation and/or rigorous laboratory conditions. This paper presents a practically applicable deep learning-based method (i.e., BloodNet) for efficient, accurate, and costless TSD inference from a macroscopic view, i.e., by using easily accessible bloodstain photos. To this end, we established a benchmark database containing around 50,000 photos of bloodstains with varying TSDs. Capitalizing on such a large-scale database, BloodNet adopted attention mechanisms to learn from relatively high-resolution input images the localized fine-grained feature representations that were highly discriminative between different TSD periods. Also, the visual analysis of the learned deep networks based on the Smooth Grad-CAM tool demonstrated that our BloodNet can stably capture the unique local patterns of bloodstains with specific TSDs, suggesting the efficacy of the utilized attention mechanism in learning fine-grained representations for TSD inference. As a paired study for BloodNet, we further conducted a microscopic analysis using Raman spectroscopic data and a machine learning method based on Bayesian optimization. Although the experimental results show that such a new microscopic-level approach outperformed the state-of-the-art by a large margin, its inference accuracy is significantly lower than BloodNet, which further justifies the efficacy of deep learning techniques in the challenging task of bloodstain TSD inference. Our code is publically accessible via https://github.com/shenxiaochenn/BloodNet. Our datasets and pre-trained models can be freely accessed via https://figshare.com/articles/dataset/21291825.

Attenuated total reflection-Fourier transform infrared spectroscopy: a universal analytical technique with promising applications in forensic analyses

Contemporary criminal investigations are based on the statements made by the victim and the eyewitnesses. They also rely on the physical evidences found in the crime scene. These evidences, and more particularly biological ones, have a great judicial value in the courtroom. They are usually used to revoke the suspect’s allegations and confirm or refute the statements made by the victim and the witnesses. Stains of body fluids are biological evidences highly sought by forensic investigators. In many criminal cases, the success of the investigation relies on the correct identification and classification of these stains. Therefore, the adoption of reliable and accurate forensic analytical methods seems to be of vital importance to attain this objective. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) is a modern and universal analytical technique capable of fingerprint recognition of the analyte using minimal amount of the test sample. The current systematic review aims to through light on the fundamentals of this technique and to illustrate its wide range of applications in forensic investigations. ATR-FTIR is a nondestructive technique which has demonstrated an exceptional efficiency in detecting, identifying and discriminating between stains of various types of body fluids usually encountered in crime scenes. The ATR-FTIR spectral data generated from bloodstains can be used to deduce a wealth of information related to the donor species, age, gender, and race. These data can also be exploited to discriminate between stains of different types of bloods including menstrual and peripheral bloods. In addition, ATR-FTIR has a great utility in the postmortem investigations. More particularly, in estimating the postmortem interval and diagnosing death caused by extreme weather conditions. It is also useful in diagnosing some ambiguous death causes such as fatal anaphylactic shock and diabetic ketoacidosis.

Comparative Study of Sample Carriers for the Identification of Volatile Compounds in Biological Fluids Using Raman Spectroscopy

Vibrational spectroscopic techniques and especially Raman spectroscopy are gaining ground in substituting the officially established chromatographic methods in the identification of ethanol and other volatile substances in body fluids, such as blood, urine, saliva, semen, and vaginal fluids. Although a couple of different carriers and substrates have been employed for the biochemical analysis of these samples, most of them are suffering from important weaknesses as far as the analysis of volatile compounds is concerned. For this reason, in this study three carriers are proposed, and the respective sample preparation methods are described for the determination of ethanol in human urine samples. More specifically, a droplet of the sample on a highly reflective carrier of gold layer, a commercially available cuvette with a mirror to enhance backscattered radiation sealed with a lid, and a home designed microscope slide with a cavity coated with gold layer and covered with transparent cling film have been evaluated. Among the three proposed carriers, the last one achieved a quick, simple, and inexpensive identification of ethanol, which was used as a case study for the volatile compound, in the biological samples. The limit of detection (LoD) was found to be 1.00 μL/mL, while at the same time evaporation of ethanol was prevented.

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.

Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.

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.

Dynamic confocal Raman spectroscopy of flowing blood in bionic blood vessel

How to quickly and safely identify blood species has always been an urgent problem for scientists. Smear test method has the risk of blood contamination, and the blood itself may carry some unknown viruses or pathogens, which will bring health risks to the testing personnel. Therefore, in order to meet the urgent needs of rapid and safe detection of blood, a technology which can detect dynamic confocal Raman spectroscopy of flowing blood in bionic blood vessel was proposed. The blood, which was sealed in the bionic blood vessel, flowed through the focus gaze area of laser by the microfluidic pump, to detect the dynamic blood Raman spectrum. Human blood and cattle blood were selected as experimental objects, and the experiments were carried out under the same parameters. Combined with PCA-LDA (principal component analysis and linear discriminate analysis) classification model, the predictive classification of the two species without error recognition was realized. The hidden weak Raman signals were mined by derivative spectra, and the fundamental differences of Raman spectra of two species were compared. Then the biochemical information that caused the differences was also analyzed. The results show the method can meet the detection requirements of sealed blood, and the Raman spectra of flowing blood is more representative than those of static blood.

Feature Selection and Rapid Characterization of Bloodstains on Different Substrates

Establishing the precise timeline of a crime can be challenging as current analytical techniques used suffer from many limitations and are destructive to the body fluids encountered at crime scenes. Raman spectroscopy has demonstrated excellent potential in forensic science as it provides direct information about the structural and molecular changes without the need for processing or extracting samples. However, its current applicability is limited to pure body fluids, as signals from the substrate underlying these fluids greatly influence the current models used for age estimation. In this study, we utilized Raman spectroscopy to identify selective spectral markers that delineate the bloodstain age in the presence of interfering signals from the substrate. The pure bloodstains and the bloodstains on the substrate were aged for two weeks at 21 ± 2 ℃ in the dark. Least absolute shrinkage and selection operator (LASSO) regression was employed to guide the feature selection in the presence of interference from substrates to accurately predict the bloodstain age. Substrate-specific regression models guided by an automated feature selection algorithm yielded low values of predictive root mean square error (0.207, 0.204, 0.222 h in logarithmic scale) and high R² (0.924, 0.926, 0.913) on test data consisting of blood spectra on floor tile, facial tissue, and linoleum-polymer substrates, respectively. This framework for an automated feature selection algorithm relies entirely on pure bloodstain spectra to train substrate-specific models for estimating the age of composite (blood on substrate) spectra. The model can thus be easily applied to any new composite spectra and is highly scalable to new environments. This study demonstrates that Raman spectroscopy coupled with LASSO could serve as a reliable and nondestructive technique to determine the age of bloodstains on any surface while aiding forensic investigations in real-world scenarios.

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.

Raman Scattering: From Structural Biology to Medical Applications

This is a review of relevant Raman spectroscopy (RS) techniques and their use in structural biology, biophysics, cells, and tissues imaging towards development of various medical diagnostic tools, drug design, and other medical applications. Classical and contemporary structural studies of different water-soluble and membrane proteins, DNA, RNA, and their interactions and behavior in different systems were analyzed in terms of applicability of RS techniques and their complementarity to other corresponding methods. We show that RS is a powerful method that links the fundamental structural biology and its medical applications in cancer, cardiovascular, neurodegenerative, atherosclerotic, and other diseases. In particular, the key roles of RS in modern technologies of structure-based drug design are the detection and imaging of membrane protein microcrystals with the help of coherent anti-Stokes Raman scattering (CARS), which would help to further the development of protein structural crystallography and would result in a number of novel high-resolution structures of membrane proteins—drug targets; and, structural studies of photoactive membrane proteins (rhodopsins, photoreceptors, etc.) for the development of new optogenetic tools. Physical background and biomedical applications of spontaneous, stimulated, resonant, and surface- and tip-enhanced RS are also discussed. All of these techniques have been extensively developed during recent several decades. A number of interesting applications of CARS, resonant, and surface-enhanced Raman spectroscopy methods are also discussed.

Human red blood cell behaviour in hydroxyethyl starch: probed by single cell spectroscopy

Hydroxyethyl starch (HES) is a commonly used intravenous fluid in hospital settings. The merits and demerits of its application is still a debatable topic. Investigating the interaction of external agents like intravenous fluids with blood cells is of great significance in clinical environments. Micro-Raman spectroscopy combined with an optical tweezers technique has been utilized for conducting systematic investigations of single live red blood cells (RBCs) under the influence of external stress agents. The present work deals with a detailed biophysical study on the response of human live red blood cells in hydroxyethyl starch using optical techniques. Morphological changes in red blood cells were monitored using quantitate phase imaging techniques. Micro-Raman studies suggest that there is a significant reduction in the oxy-haemoglobin level in red blood cells suspended in HES. The spectra recorded by using different probe laser powers has shown that the cells are more vulnerable in HES under the influence of externally induced stress than in blood plasma. In addition, the spectral results support the possibility of heme aggregation and membrane damage for red blood cells in HES under externally induced stress. Principle component analysis performed on the Raman spectra were able to effectively discriminate between red blood cells in HES and in blood plasma. The use of Raman tweezers can be highly beneficial in elucidating biochemical alterations happening in live, human red blood cell.

Hydroxyethyl starch (HES) is a commonly used intravenous fluid in hospital settings.

Blood species identification based on deep learning analysis of Raman spectra

Blood analysis is an indispensable means of detection in criminal investigation, customs security and quarantine, anti-poaching of wildlife, and other incidents. Detecting the species of blood is one of the most important analyses. In order to classify species by analyzing Raman spectra of blood, a recognition method based on deep learning principle is proposed in this paper. This method can realize multi-identification blood species, by constructing a one-dimensional convolution neural network and establishing a Raman spectra database containing 20 kinds of blood. The network model is obtained through training, and then is employed to predict the testing set data. The average accuracy of blind detection is more than 97%. In this paper, we try to increase the diversity of data to improve the robustness of the model, optimize the network and adjust the hyperparameters to improve the recognition ability of the model. The evaluation results show that the deep learning model has high recognition performance to distinguish the species of blood.

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.

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.

Differentiating Donor Age Groups Based on Raman Spectroscopy of Bloodstains for Forensic Purposes

Developments in analytical chemistry technologies and portable instrumentation over the past decade have contributed significantly to a variety of applications ranging from point of care testing to industrial process control. In particular, Raman spectroscopy has advanced for analyzing various types of evidence for forensic purposes. Extracting phenotypic information (e.g., sex, race, age, etc.) from body fluid traces is highly desirable for criminal investigations. Identifying the chronological age (CA) of a blood donor can provide significant assistance to detectives. In this proof-of-concept study, Raman spectroscopy and chemometrics have been used to analyze blood from human donors, and differentiate between them based on their CA [i.e., newborns (CA of <1 year), adolescents (CA of 11-13 years), and adults (CA of 43-68 years)]. A support vector machines discriminant analysis (SVMDA) model was constructed, which demonstrated high accuracy in correctly predicting blood donors' age groups where the lowest cross-validated sensitivity and specificity values were 0.96 and 0.97, respectively. Overall, this preliminary study demonstrates the high selectivity of Raman spectroscopy for differentiating between blood donors based on their CA. The demonstrated capability completes our suite of phenotype profiling methodologies including the determination of sex and race. CA determination has particular importance since this characteristic cannot be determined through DNA profiling unlike sex and race. When completed, the developed methodology should allow for phenotype profiling based on dry traces of body fluids immediately at the scene of a crime. The availability of this information within the first few hours since the crime discovery could be invaluable for the investigation.

Soft and Robust Identification of Body Fluid Using Fourier Transform Infrared Spectroscopy and Chemometric Strategies for Forensic Analysis

Body fluid (BF) identification is a critical part of a criminal investigation because of its ability to suggest how the crime was committed and to provide reliable origins of DNA. In contrast to current methods using serological and biochemical techniques, vibrational spectroscopic approaches provide alternative advantages for forensic BF identification, such as non-destructivity and versatility for various BF types and analytical interests. However, unexplored issues remain for its practical application to forensics; for example, a specific BF needs to be discriminated from all other suspicious materials as well as other BFs, and the method should be applicable even to aged BF samples. Herein, we describe an innovative modeling method for discriminating the ATR FT-IR spectra of various BFs, including peripheral blood, saliva, semen, urine and sweat, to meet the practical demands described above. Spectra from unexpected non-BF samples were efficiently excluded as outliers by adopting the Q-statistics technique. The robustness of the models against aged BFs was significantly improved by using the discrimination scheme of a dichotomous classification tree with hierarchical clustering. The present study advances the use of vibrational spectroscopy and a chemometric strategy for forensic BF identification.