At crime scenes, various methods can be used to determine how much time has passed since the act happened, for example a body left at the scene, camera footage or eye witnesses. In this thesis, however, a different method shall be used to determine the time of the crime. The time will be determined using blood evidence.

To be exact, the goal of this thesis is to analyse the aging of bloodstains through colour analysis, and constructing a Bayesian network (BN) which can accurately make predictions on the time of deposition of a bloodstain.

The data used for the construction of the BNs was obtained from images provided by the Leiden Institute of Physics (LION). To obtain the data, the bloodstains in the images first need to be isolated from the background. Afterwards, the bloodstains are split into two parts: the inner part of the bloodstain and the complete bloodstain. Both the isolation and the splitting are done using a method called masking. Afterwards they can be converted to RGB (red, green, blue) values and using these RGB values, the following data can be collected for each colour channel of both the inner part of the bloodstain and the whole bloodstain: mean, min, max, variance, and the 5%, 20%, 50%, 80% and 95% quantiles.

Using various subsets of the data, BNs can be constructed. The structure of the BN is determined using a structure-learning algorithm called hill-climbing . To determine the validity of a given structure k-fold cross-validation can be performed using a given loss function. In this thesis, k=5 has been used, and the Mean Squared Error (MSE) has been taken as the loss function. Upon comparison of the MSE, it seems that the best model is given by the red values of a bloodstain. However, even the best performing model found in this thesis still has a considerably poor performance as the BN for the red variables has an MSE of 15149.81.