EXPRESS: Age estimation of bloodstains based on Kernel Extreme Learning Machine algorithm and Near-infrared spectroscopy technology

Bloodstains are among the most common forms of evidence at crime scenes and are considered one of the most critical types of physical evidence in forensic science. Accurately determining the age of bloodstains at a crime scene is crucial to solving cases. Traditionally, this determination relied on the expertise of forensic investigators or biochemical analysis methods, both of which often damaged the samples and increased investigation costs. This necessitated the development of a rapid, non-destructive, and accurate method for bloodstain age estimation. In the proposed method, bloodstains are applied to various substrates to minimize the influence of background interference on the experimental results. Spectral data are extracted non-destructively from the bloodstains and integrated with machine learning techniques to develop predictive models.The training model of blood aging was built using the Kernel Extreme Learning Machine (KELM) algorithm and the near-infrared (NIR) spectral data. It was also compared with the traditional Partial Least Squares Regression (PLS-R), Random Forest (RF) models and Back-propagation neural network (BP) models. The experimental results showed that the performance of KELM model was the best, test set with a determination coefficient (R 2 ) of 0.94, which is higher compared to that of the PLS-R model (R 2 = 0.75), RF model (R 2 = 0.82) and BP model (R 2 = 0.90). Besides, the root mean square error of prediction (RMSEP) of the KELM model was 2.54 days, smaller than the PLS-R model (RMSEP = 5.18 days) , the RF model (RMSEP = 4.29days) and the BP model (RMSEP = 3.68 days).The mean absolute percentage error (MAPE) of the validation set was 2.45% when using the KELM algorithm, which was also the minimum of the three algorithms, and it represented that the prediction results were the best. The experimental data showed that the method proposed in this study could accurately estimate the age of bloodstains, providing a new technology reference for bloodstain detection.