Accurate forensic identification of asphyxial deaths: Differentiating strangulation and drowning using ATR-FTIR spectroscopy
Year: 2025
Type: article
Abstract: Asphyxial death is a leading cause of violent death, strangulation is more frequently seen in murder and domestic abuse cases, while drowning is one of the most common causes of accidental death. Therefore, studies on these two causes of death are beneficial to ensuring the administration of justice and increasing detection rate of criminal cases. This study investigated the viability of ATR-FTIR spectroscopy as a method for distinguishing between strangulation and drowning in autolysis and putrefaction cadavers of forensic cases. Utilizing C57BL/6 mice death models, lung tissue samples were analyzed post-mortem using ATR-FTIR spectroscopy, alongside traditional histological methods such as Hematoxylin and Eosin (HE) staining. Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLSDA) were employed to enhance the spectral data interpretation, demonstrating high accuracy in distinguishing between the two causes of death. The PLSDA model showed a 100 % accuracy in external validations for strangulation and drowning cause of death identification, highlighting the potential of ATR-FTIR spectroscopy combined with machine learning algorithms in forensic applications. The results revealed distinct biochemical signatures for strangulation and drowning, with significant changes observed in the spectral peaks associated with Amide band, protein secondary structures and nucleic acids. The integration of ATR-FTIR spectroscopy provides a non-destructive, rapid, and reliable method for forensic investigators, especially when traditional autopsy results are inconclusive due to autolysis and putrefaction. The study emphasizes how crucial it is to combine ATR-FTIR spectroscopic with histopathological examination and chemometrics to increase the precision and dependability of forensic investigations and support the administration of justice.
Source: Science & Justice
Authors: Hao Wu, Ruina Liu, Gongji Wang, Chen Shen, Xinggong Liang, Run Chen, Mingyan Deng, Shuo Wu, Kai Zhang, Zhenyuan Wang
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,720
Award: 基于光、质谱分析及微生物组学技术推断死亡时间的实验研究
OpenAlex ID: W4409430337
