New perspective: An in vitro study on inferring the post-mortem interval (PMI) of human skeletal muscle based on ATR-FTIR spectroscopy combined with machine learning

The determination of the PMI remains one of the most critical challenges within the field of forensic science. Nonetheless, the estimation of PMI has emerged as one of the most complex and challenging domains of research within the field, primarily due to the absence of precise and dependable methodologies, coupled with the advanced decomposition processes of cadavers. To address these challenges, researchers have tried many methods, such as insects, microorganisms, body fluids, and animal tissues. Nevertheless, there is a paucity of research specifically examining human skeletal muscle. In this study, we collected a substantial number of human skeletal muscle samples and employed ATR-FTIR spectroscopy combined with multiple machine learning to analyze spectral changes in skeletal muscle across different PMIs. These methods primarily included PLS-R, PCR, CLS-R, MLR, SVR, and XGB-R to predict the PMI of human skeletal muscle. The results revealed distinct spectral variations in the protein, carbohydrate, and nucleic acid regions of skeletal muscle across different PMIs. Notably, the absorption peak at the 1540 cm⁻¹ amide II band exhibited a gradual decline over time, demonstrating significant potential for PMI estimation and decomposition tracking. Due to its operational simplicity, rapid analysis, and cost-effectiveness, ATR-FTIR spectroscopy combined with machine learning demonstrates significant practical value and forensic potential for PMI estimation.