Identification of pulmonary edema in forensic autopsy cases of fatal anaphylactic shock using Fourier transform infrared microspectroscopy

Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms

The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with machine learning. ATR-FTIR offers non-destructive and non-invasive advantages, requiring minimal sample preparation. By detecting specific chemical bonds in blood components, it enables the differentiation of various body fluids. However, the subjective interpretation of the spectra poses challenges in distinguishing different fluids. To address this, we employ machine learning techniques. Machine learning is extensively used in chemometrics to analyze chemical data, build models, and extract useful information. This includes both unsupervised learning and supervised learning methods, which provide objective characterization and differentiation. The focus of this study was to identify human and porcine blood on substrates using ATR-FTIR spectroscopy. The substrates included paper, plastic, cloth, and wood. Data preprocessing was performed using Principal Component Analysis (PCA) to reduce dimensionality and analyze latent variables. Subsequently, six machine learning algorithms were used to build classification models and compare their performance. These algorithms comprise Partial Least Squares Discriminant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performance of different identification models still varies based on the type of substrate. The integration of these disciplines enables researchers to harness the power of data-driven approaches for solving complex forensic problems. The objective differentiation of bloodstains using machine learning holds significant implications for criminal investigations. This technique offers a non-destructive, simple, selective, and rapid approach for forensic analysis, thereby assisting forensic scientists and investigators in determining crucial evidence related to bloodstains.

The Role of Protein Degradation in Estimation Postmortem Interval and Confirmation of Cause of Death in Forensic Pathology: A Literature Review

It is well known that proteins are important bio-macromolecules in human organisms, and numerous proteins are widely used in the clinical practice, whereas their application in forensic science is currently limited. This limitation is mainly attributed to the postmortem degradation of targeted proteins, which can significantly impact final conclusions. In the last decade, numerous methods have been established to detect the protein from a forensic perspective, and some of the postmortem proteins have been applied in forensic practice. To better understand the emerging issues and challenges in postmortem proteins, we have reviewed the current application of protein technologies at postmortem in forensic practice. Meanwhile, we discuss the application of proteins in identifying the cause of death, and postmortem interval (PMI). Finally, we highlight the interpretability and limitations of postmortem protein challenges. We believe that utilizing the multi-omics method can enhance the comprehensiveness of applying proteins in forensic practice.

Distinguishing Asphyxia from Sudden Cardiac Death as the Cause of Death from the Lung Tissues of Rats and Humans Using Fourier Transform Infrared Spectroscopy

The ability to determine asphyxia as a cause of death is important in forensic practice and helps us to judge whether a case is criminal. However, in some cases where the deceased has underlying heart disease, death by asphyxia cannot be determined by traditional autopsy and morphological observation under a microscope because there are no specific morphological features for either asphyxia or sudden cardiac death (SCD). Here, Fourier transform infrared (FTIR) spectroscopy was employed to distinguish asphyxia from SCD. A total of 40 lung tissues (collected at 0 h and 24 h postmortem) from 20 rats (10 died from asphyxia and 10 died from SCD) and 16 human lung tissues from 16 real cases were used for spectral data acquisition. After data preprocessing, 2675 spectra from rat lung tissues and 1526 spectra from human lung tissues were obtained for subsequent analysis. First, we found that there were biochemical differences in the rat lung tissues between the two causes of death by principal component analysis and partial least-squares discriminant analysis (PLS-DA), which were related to alterations in lipids, proteins, and nucleic acids. In addition, a PLS-DA classification model can be built to distinguish asphyxia from SCD. Second, based on the spectral data obtained from lung tissues allowed to decompose for 24 h, we could still distinguish asphyxia from SCD even when decomposition occurred in animal models. Nine important spectral features that contributed to the discrimination in the animal experiment were selected and further analyzed. Third, 7 of the 9 differential spectral features were also found to be significantly different in human lung tissues from 16 real cases. A support vector machine model was finally built by using the seven variables to distinguish asphyxia from SCD in the human samples. Compared with the linear PLS-DA model, its accuracy was significantly improved to 0.798, and the correct rate of determining the cause of death was 100%. This study shows the application potential of FTIR spectroscopy for exploring the subtle biochemical differences resulting from different death processes and determining the cause of death even after decomposition.

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.

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.

Identification of antemortem, perimortem and postmortem fractures by FTIR spectroscopy based on a rabbit tibial fracture model

The identification of antemortem, perimortem and postmortem fractures is very important for forensic pathologists and anthropologists. However, traditional methods are subjective, time-consuming, and have low accuracy, which do not fundamentally solve the problem. In this study, we utilized Fourier transform infrared (FTIR) spectroscopy and chemometrics to identify antemortem, perimortem and postmortem fractures in a rabbit tibial fracture model. Based on the results of the principal component analysis (PCA), changes in the ante-perimortem fracture repair process are mainly associated with protein variations, while postmortem fractures are more likely to result in lipid changes during degradation. Then, a partial least squares discriminant analysis (PLS-DA) was performed to assess the classification ability of the training and predictive datasets, with classification accuracies of 88.9% and 86.7%, respectively. According to the latent variable 1 (LV1) loading plot, amide I and amide II (proteins) are mostly classified as ante-perimortem and postmortem fractures. In conclusion, FTIR spectroscopy is a reliable tool to identify antemortem, perimortem and postmortem fractures. FTIR has the advantages of rapid, objective and strong discrimination. and shows great potential for analyzing forensic cases under actual natural conditions.

Identifying muscle hemorrhage in rat cadavers with advanced decomposition by FT-IR microspectroscopy combined with chemometrics

The identification of muscle hemorrhage in a cadaver that is in an advanced stage of decomposition is an important but challenging task. Our study investigated whether Fourier transform infrared (FT-IR) microspectroscopy in conjunction with chemometrics could identify muscle hemorrhage using rat cadavers with advanced decomposition. In this study, an intramuscular blood injection method, instead of a mechanical injury method, was used to construct a muscle hemorrhage model, and the modeling idea of muscle hemorrhage identification was to discriminate and classify hemoglobin-leaking myofibrils from negative myofibrils. First, the optical images of hematoxylin/eosin (H&E) stained hemorrhagic muscle at different postmortem intervals (PMIs) were observed and showed that the morphological features of whole erythrocytes disappeared since the PMI of 4 d. Subsequently, principle component analysis (PCA) was performed and indicated that the biochemical differences in protein structures between fresh erythrocytes and myofibrils can be detected by the IR spectroscopic method. Ultimately, several classification models based on the partial least square discriminant analysis (PLS-DA) algorithm were successfully constructed for different PMIs and PMI ranges and achieved great prediction performances in external validations. This preliminary study demonstrates the feasibility of using FT-IR microspectroscopy combined with chemometrics as a potential approach for identifying muscle hemorrhage in cadavers with advanced decomposition for offering vital evidences in judicial process.

Determination of causes of death via spectrochemical analysis of forensic autopsies-based pulmonary edema fluid samples with deep learning algorithm

This study investigated whether infrared spectroscopy combined with a deep learning algorithm could be a useful tool for determining causes of death by analyzing pulmonary edema fluid from forensic autopsies. A newly designed convolutional neural network-based deep learning framework, named DeepIR and eight popular machine learning algorithms, were used to construct classifiers. The prediction performances of these classifiers demonstrated that DeepIR outperformed the machine learning algorithms in establishing classifiers to determine the causes of death. Moreover, DeepIR was generally less dependent on preprocessing procedures than were the machine learning algorithms; it provided the validation accuracy with a narrow range from 0.9661 to 0.9856 and the test accuracy ranging from 0.8774 to 0.9167 on the raw pulmonary edema fluid spectral dataset and the nine preprocessing protocol-based datasets in our study. In conclusion, this study demonstrates that the deep learning-equipped Fourier transform infrared spectroscopy technique has the potential to be an effective aid for determining causes of death.

Postmortem diagnosis of fatal hypothermia/hyperthermia by spectrochemical analysis of plasma

Postmortem diagnosis of extreme-weather-related deaths is a challenging forensic task. Here, we present a state-of-the-art study that employed attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy in combination with Chemometrics for postmortem diagnosis of fatal hypothermia/hyperthermia by biochemical investigation of plasma in rats. The results of principal component analysis (PCA) and spectral analysis revealed that plasma samples from the fatal hypothermia, fatal hyperthermia, and control groups, are substantially different from each other based on the spectral variations associated with the lipid, carbohydrate and nucleic acid components. Two partial least squares-discriminant analysis (PLS-DA) classification models (hypothermia-nonhypothermia and hyperthermia-nonhyperthermia binary models) with a 100% accuracy rate were constructed. Subsequently, internal cross-validation was performed to assess the robustness of these two models, which resulted in 98.1 and 100% accuracy. Ultimately, classification predictions of 42 unknown plasma samples were performed by these two models, and both models achieved 100% accuracy. Additionally, our results demonstrated that hemolysis and postmortem hypothermic/hyperthermic effects did not weaken the prediction ability of these two classification models. In summary, this work demonstrates ATR-FTIR spectroscopy's great potential for postmortem diagnosis of fatal hypothermia/hyperthermia.

Biochemical detection of fatal hypothermia and hyperthermia in affected rat hypothalamus tissues by Fourier transform infrared spectroscopy

It is difficult to determinate the cause of death from exposure to fatal hypothermia and hyperthermia in forensic casework. Here, we present a state-of-the-art study that employs Fourier-transform infrared (FTIR) spectroscopy to investigate the hypothalamus tissues of fatal hypothermic, fatal hyperthermic and normothermic rats to determine forensically significant biomarkers related to fatal hypothermia and hyperthermia. Our results revealed that the spectral variations in the lipid, protein, carbohydrate and nucleic acid components are highly different for hypothalamuses after exposure to fatal hypothermic, fatal hyperthermic and normothermic conditions. In comparison with the normothermia group, the fatal hypothermia and hyperthermia groups contained higher total lipid amounts but were lower in unsaturated lipids. Additionally, their cell membranes were found to have less motional freedom. Among these three groups, the fatal hyperthermia group contained the lowest total proteins and carbohydrates and the highest aggregated and dysfunctional proteins, while the fatal hypothermia group contained the highest level of nucleic acids. In conclusion, this study demonstrates that FTIR spectroscopy has the potential to become a reliable method for the biochemical characterization of fatal hypothermia and hyperthermia hypothalamus tissues, and this could be used as a postmortem diagnostic feature in fatal hypothermia and hyperthermia deaths.