Environment aging analysis of animal bloodstains with ATR-FTIR and CNN

Abstract

Bloodstain analysis is a critical component of forensic science, particularly for determining the time of deposition and understanding the effects of environmental conditions on evidence. This study presents an innovative bloodstains environment aging model, which integrates attenuated total reflection fourier transform infrared spectroscopy (ATR-FTIR) with a convolutional neural network (CNN) optimized using the black-winged kite algorithm. Bloodstains from common sources (pig, cow, and chicken) were analyzed under varying environmental conditions, including temperature fluctuations (0 °C, 40 °C, 100 °C) and simulated sunlight exposure, across multiple aging periods (1, 2, 4, 8 days). Spectral data obtained through ATR-FTIR scanning served as the input for the optimized CNN, enabling precise differentiation and classification of bloodstains based on aging and environmental factors. The model achieved high predictive accuracy, with 97.86 % for pig blood, 95.47 % for cow blood, and 97.15 % for chicken blood under 0 °C conditions, demonstrating its robustness and reliability in forensic applications. Additionally, this research highlights the potential for integrating spectroscopic data with advanced deep learning techniques to enhance forensic methodologies. By improving accuracy, accessibility, and cost-effectiveness, this work represents a significant advancement in bloodstain analysis and forensic science.