Evaluation of postmortem interval (PMI) is of paramount importance to guide criminal investigations, especially when witnesses are not found. However, accurate PMI estimation is a challenging task in the forensic community due to the limitations of existing methods. The study aims to investigate the potential of attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy for predicting PMI based on vitreous humor (VH). VH samples were collected from 72 rabbits in the range of 0-48 h postmortem at a 6-h interval. Their FTIR spectra were normalized by the extended multiplicative signal correction (RMSC) and divided into calibration and validation sets. After analysis of the absorption bands, the Bayesian ridge regression (BRR), support vector regression (SVR), and artificial neural network (ANN) methods were established by the calibration set using a 10-fold cross-validation that was further used to predict the PMI in the validation set. The validity of the models was assessed by a permutation test. The current study demonstrated that multiple macromolecules in the VH samples were reflected in a FTIR spectrum, and the spectral absorption bands at 1313 and 925 cm-1 were highly correlated with PMI. The three models allowed generalization to the validation set due to similar R2 and errors between the calibration and validation tests. The highest accuracy with R2 = 0.983 and error = 2.018 h was achieved by the ANN model in the validation test. The results suggest that ATR-FTIR spectroscopy may be useful for VH analysis in order to predict PMI in the future. Graphical abstract ᅟ.
Keywords: Forensic medicine; Fourier transform infrared spectroscopy; Machine learning; Postmortem interval; Vitreous humor.