Overcoming obstacles: Analysis of blood and semen stains washed with different chemicals with ATR-FTIR

A review on forensic analysis of bio fluids (blood, semen, vaginal fluid, menstrual blood, urine, saliva): Spectroscopic and non-spectroscopic technique

The accurate detection, identification, and analysis of biofluids at crime scenes play a critical role in forensic investigations. Various biofluids, such as blood, semen, vaginal fluid, menstrual blood, urine, and saliva, can be crucial evidence. In a murder case involving a knife attack, for instance, bloodstains from both the victim and perpetrator might be present. Sexual assault cases often involve the analysis of semen and vaginal secretions. Biofluid analysis employs a two-tiered approach: presumptive tests for initial identification and confirmatory tests for definitive analysis. This review article focuses on six key biofluids and their forensic significance. In this review, we comprehensively explore the relevant analytical techniques, including non-spectroscopic methods like immunoassays, spot tests, and cytokine profiling, alongside spectroscopic techniques such as Infrared (IR) spectroscopy, Mass Spectrometry (MS), and Raman Spectroscopy (RS).

A comparative study of in vivo toxicity in zebrafish embryos synthesized CuO nanoparticles characterized from Salacia reticulata

The Salacia reticulata, a medicinal woody climbing shrub, was utilized for our study, the green synthesis of CuO nanoparticles, which were analyzed through SEM, EDX, FTIR, XRD, and UV‒Vis spectroscopy. This study assessed the toxicity to zebrafish embryos and explored the antibacterial, cytotoxic, antidiabetic, and anti-inflammatory properties of the synthesized nanoparticles. In results, the UV absorption of the CuO NPs showed that the intensity of nanoparticle green colloidal suspension changed from blue to green, which also confirmed that the spectrum of the green CuO NPs changed from colorless to black. in FT-IR and XRD spectral analysis to identify functional groups and determine the particle size of CuO NPs prepared by green and chemical methods. Its showed that CuO NPs (green) had a size of approximately 42.2 nm, while CuO NPs (chemical) had a size of approximately 84 nm. The morphology of these NPs was analyzed using SEM-EDX. Compared with their chemically prepared counterparts, the green-synthesized CuO nanoparticles demonstrated superior dispersion. Additionally, both green and chemical CuO nanoparticles at a concentration of 200 µL/mL caused developmental anomalies and increased mortality in zebrafish embryos and larvae. The green and chemical CuO NPs inhibited α-glucosidase enzyme activity at concentrations between 10 and 50 µL/mL, with IC50 values of 22 µL/mL and 26 µL/mL, respectively. The extract exhibited anti-inflammatory activity, with IC50 values of 274 and 109 µL/mL. The authors concluded that this green nanoparticle method has potential as a more eco-friendly and cost-effective alternative to traditional synthetic methods. NPs are widely used in human contact fields (medicine and agriculture), hence synthesis methods that do not involve toxic substances are becoming increasingly important.

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.

Estimation of time since deposition of semen stain on different fabric types using ATR-FTIR spectroscopy and chemometrics

Various body fluids such as blood, semen, vaginal secretions, and saliva are frequently encountered at crime scene. In cases of sexual assault, semen stains are one of the most reliable evidence of biological origin. In this study, our objective was to develop a method for estimating the time since deposition of semen stains on five different fabric types using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy, with a focus on a time frame of up to 8 weeks. Semen samples from six different volunteers were dripped onto five distinct fabric materials, and ATR-FTIR measurements were obtained at 17 different time points. Principal component analysis (PCA) and partial least squares (PLS) methods were employed to differentiate semen stains on various fabric samples and estimate the age of semen stains. Models constructed using PCA and PLSR achieved high R2 values and low root-mean-square error (RMSE). While the performance varies depending on fabric types, it was observed that age estimation of semen stains can be made within following intervals: 0.39-0.76 days for 0-7 day range, 2.59-3.38 days for the 1-8 week range, and 3.98-8.1 days for the 0-56 day range. This study demonstrates the effectiveness of using ATR-FTIR spectroscopy in combination with chemometrics to estimate the age of human semen stains on various fabric types based on time-dependent spectral changes.