Summary: Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis , Anaerosalibacter bizertensis , Lactobacillus reuteri , and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 ± 0.8 h within 24‐h decomposition and 14.5 ± 4.4 h within 15‐day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.

Estimation of the age of human bloodstains is of great importance in forensic practices, but it is a challenging task because of the lack of a well-accepted, reliable, and established method. Here, the attenuated total reflection (ATR)-Fourier transform infrared (FTIR) technique combined with advanced chemometric methods was utilized to determine the age of indoor and outdoor bloodstains up to 107 days. The bloodstain storage conditions mimicked crime scene scenarios as closely as possible. Two partial least squares regression models—indoor and outdoor models with 7–85 days—exhibited good performance for external validation, with low values of predictive root mean squared error (5.83 and 4.77) and high R² values (0.94 and 0.96) and residual predictive deviation (4.08 and 5.14), respectively. Two partial least squares–discriminant analysis classification models were built and demonstrated excellent distinction between fresh (age ≤1 d) and older (age >1 d) bloodstains, which is highly valuable for forensic investigations. These findings demonstrate that ATR-FTIR spectroscopy coupled with advanced chemometric methods can be employed as a rapid and non-destructive tool for age estimation of bloodstains in real-world forensic investigation.

The determination of time since death or the postmortem interval (PMI) is one of the most important and frequently asked questions in forensic medicine. Medicolegal scholars and forensic pathologists around the world have studied the estimation of PMI extensively in the past, and many novel methods and advanced technologies have now been applied in the field. For several centuries, Chinese forensic examiners have also worked on the estimation of the PMI, and there are a large number of excellent studies published in Chinese rather than in English, and these are not easily accessible or known internationally. Therefore we have conducted a review of relevant studies published by Chinese forensic scholars in the last few decades. The scope of this review is to provide a concise summary of the current progress in the estimation of PMI by Chinese forensic researchers using molecular biology, spectroscopic technology, entomological methods, energy changes, thanatochemistry and other methods.

Postmortem interval (PMI) evaluation remains a challenge in the forensic community due to the lack of efficient methods. Studies have focused on chemical analysis of biofluids for PMI estimation; however, no reports using spectroscopic methods in pericardial fluid (PF) are available. In this study, Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) accessory was applied to collect comprehensive biochemical information from rabbit PF at different PMIs. The PMI-dependent spectral signature was determined by two-dimensional (2D) correlation analysis. The partial least square (PLS) and nu-support vector machine (nu-SVM) models were then established based on the acquired spectral dataset. Spectral variables associated with amide I, amide II, COO⁻, C-H bending, and C-O or C-OH vibrations arising from proteins, polypeptides, amino acids and carbohydrates, respectively, were susceptible to PMI in 2D correlation analysis. Moreover, the nu-SVM model appeared to achieve a more satisfactory prediction than the PLS model in calibration; the reliability of both models was determined in an external validation set. The study shows the possibility of application of ATR-FTIR methods in postmortem interval estimation using PF samples.