[Establishment of the Mathematical Model for PMI Estimation Using FTIR Spectroscopy and Data Mining Method].

Area of Science:

• Forensic Science
• Biophysics
• Analytical Chemistry

Background:

• Accurate postmortem interval (PMI) estimation is crucial in forensic investigations.
• Traditional methods for PMI determination have limitations in precision and applicability.

Purpose of the Study:

• To analyze the relationship between Fourier transform infrared (FTIR) spectrum of rat spleen tissue and PMI.
• To develop a method for PMI estimation using FTIR spectroscopy combined with data mining.

Main Methods:

• Rats' spleen tissues were analyzed using FTIR spectroscopy at various time points post-sacrifice.
• Data mining techniques, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Support Vector Machine Classification (SVMC), were employed.
• Partial Least Squares (PLS) regression was used to establish a PMI estimation model.

Main Results:

• FTIR spectral absorption peak intensity of spleen tissue varied with PMI.
• PCA revealed distinct clustering of spectral samples based on time points (96% cumulative contribution rate).
• PLS-DA and SVMC effectively classified samples into four PMI categories (0-24h, 48-72h, 96-120h, 144-168h).
• The PLS regression model achieved high accuracy in PMI estimation (R²=0.96 for calibration, R²=0.97 for prediction) with low errors (RMSEP=10.49h).

Conclusions:

• FTIR spectroscopy combined with data mining provides effective qualitative and quantitative analysis of rat spleen tissue for PMI estimation.
• Classification and PLS regression models based on FTIR spectral data can be reliably established for accurate PMI determination.