Evaluation of evidence value of glass fragments by likelihood ratio and Bayesian Network approaches.

Growing interest in applications of Bayesian Networks (BNs) in forensic science raises the question whether BN could be used in forensic practice for the evaluation of glass objects described by the results of physico-chemical analysis, especially the information obtained from analysis performed by Glass Refractive Index Measurement technique. Comparison of glass fragments, i.e. could two glass samples (recovered from, e.g. the suspect's clothes and control, collected from the scene of crime) have originated from the same object, is one of the tasks of evaluation of glass fragments for forensic purposes. The second problem is the determination of their use-type category, e.g. does an analysed glass fragment originate from an unknown window or container? This process, known as classification, is especially important when the analysed fragment was recovered from the suspect's clothes and there was no control sample. 111 glass objects (car windows, building windows, and containers) were measured in order to determine the refractive index (RI) before (RI(b)) and after the annealing process (RI(a)), from which a new variable dRI=log(10)|RI(a)-RI(b)| was calculated. Results obtained by the application of BN models were compared to results obtained by the application of suitable likelihood ratio models commonly used in the forensic sphere nowadays. The performed research showed that BN models could be satisfactorily applied to obtain the evidence value of glass fragments when RI(b) is used in the comparison problem. Use of BN with dRI in the classification problem also gave good results.