An investigation on annular cartilage samples for post-mortem interval estimation using Fourier transform infrared...

Area of Science:

• Forensic Science
• Biophysics
• Biochemistry

Background:

• Accurate post-mortem interval (PMI) estimation is vital in forensic investigations.
• Traditional methods using soft tissues or biofluids face limitations due to rapid degradation.
• Cartilage offers a more stable alternative for post-mortem analysis.

Purpose of the Study:

• To evaluate the feasibility of using Fourier transform infrared (FTIR) spectroscopy on human annular cartilage for PMI estimation.
• To develop and validate predictive models for PMI based on FTIR spectral data.
• To identify key spectral features indicative of post-mortem changes in cartilage.

Main Methods:

• Human annular cartilage samples were analyzed using FTIR spectroscopy within 30 days post-mortem.
• Principal component analysis (PCA) was employed to assess the influence of confounding factors like sex and cause of death.
• Partial least squares (PLS) regression models were developed using pre-processed spectral data for PMI prediction.

Main Results:

• PCA indicated minimal impact of sex and cause of death on post-mortem spectral variations in cartilage.
• The optimal predictive model, utilizing second derivative transform and EMSC, achieved a low prediction error of 1.49 days.
• Spectral regions associated with proteins and carbohydrates were identified as significant contributors to PMI prediction.

Conclusions:

• Cartilage-based FTIR analysis is a feasible method for estimating the post-mortem interval.
• The developed PLS regression model demonstrates satisfactory accuracy for PMI prediction within 30 days.
• Further research incorporating advanced algorithms can enhance the precision of cartilage-based PMI estimation.