Discriminating injury and estimating post-injury intervals in moderate TBI by ATR-FTIR combined with chemometrics and machine learning

Traumatic brain injury (TBI) is a common case type in forensic medicine. Accurately distinguishing injured from non-injured tissues and estimating the time elapsed since injury are crucial aspects of forensic practice. This study aimed to investigate the feasibility of using Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy to discriminate between injured and non-injured tissues, as well as to differentiate various post-injury intervals following moderate TBI. A mouse model of moderate TBI was established using the Feeney weight-drop method, and individuals with moderate injury were strictly selected based on the modified Neurological Severity Score (mNSS). Firstly, Principal Component Analysis (PCA) was employed for dimensionality reduction and visualization of the spectral data. Secondly, classification models including Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) were constructed to distinguish injured from non-injured tissues across different time points, both of which demonstrated excellent classification performance. Subsequently, regression models, including Partial Least Squares Regression (PLS-R), Constrained Linear Regression (CLR), Principal Component Regression (PCR), Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were developed based on the spectral data to predict the survival time post-TBI. Among these, the SVR model delivered the best prediction performance with R² of 0.936 and RMSE of 1.485 days. This preliminary study demonstrates that FTIR spectroscopy combined with chemometrics enables rapid and accurate identification of trauma occurrence and estimation of the post-injury interval, thereby providing a scientific basis for case investigation and judicial proceedings.