Discrimination Between Infant and Adult Bloodstains Using Micro-Raman Spectroscopy: A Preliminary Study

The application of vibrational spectroscopy in forensic analysis of biological evidence

Vibrational spectroscopy is a powerful analytical domain, within which Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy stand as exemplars, offering high chemical specificity and sensitivity. These methodologies have been instrumental in the characterization of chemical compounds for an extensive period. They are particularly adept at the identification and analysis of minute sample quantities. Both FTIR and Raman spectroscopy are proficient in elucidating small liquid samples and detecting nuanced molecular alterations. The application of chemometrics further augments their analytical prowess. Currently, these techniques are in the research phase within forensic medicine and have yet to be broadly implemented in examination and identification processes. Nonetheless, studies have indicated that a combined classification model utilizing FTIR and Raman spectroscopy yields exceptional results for the identification of biological fluid-related information and the determination of causes of death. The objective of this review is to delineate the current research trajectory and potential applications of these two vibrational spectroscopic techniques in the detection of body fluids and the ascertainment of causes of death within the context of forensic medicine.

Serum Raman spectroscopy can be used to screen patients with early rheumatoid arthritis

In this study, Raman spectroscopy was used to analyze the serum of patients with early rheumatoid arthritis (RA), and to explore the screening value of Raman spectroscopy in patients with early RA. A total of 216 patients were included in the study. Fasting venous blood was collected for routine biochemical detection, and the remaining samples were tested by serum Raman spectroscopy. Support vector machine was used for model building and training. The area under the curve (AUC) values of the model were as follows: (1) healthy group versus early RA group: 0.860, (2) healthy group versus non-early RA group: 0.903, and (3) early RA group versus non-early RA group: 0.918. This study shows that serum Raman spectroscopy has a good ability to screen RA and can be staged according to the course of the disease, which can provide new ideas and technical support for the diagnosis or screening of early RA.

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.

Study on the Raman spectral characteristics of dynamic and static blood and its application in species identification

This paper proposes a method to identify the blood of 4 poultry species (chicken, duck, goose and pigeon) based on Raman spectroscopy and its baseline. Samples were prepared by pretreatment methods of freezing, thawing, and dilution. The Raman spectra of dynamic blood and static blood were measured, respectively, and the spectral differences between the two research schemes were analyzed. The four species of poultry blood were identified based on the Raman spectroscopy and its baseline. The results show that the method can realize the identification of four species of poultry blood. In addition, the potential of Raman spectroscopy as a technique for determining carotenoids in blood has been clearly confirmed, which opens up the possibility to quickly determine whether poultry eats feed containing carotenoids without sample preparation.

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.