Chemometric estimation of post-mortem interval based on Na+ and K+ concentrations from human vitreous humour by linear least squares and artificial neural networks modelling

• Published in: Australian journal of forensic sciences Vol. 46; no. 2; pp. 166 - 179
• Main Authors: Gadzuric, Slobodan B., Podunavac Kuzmanovic, Sanja O., Jokic, Aleksandar I., Vranes, Milan B., Ajdukovic, Niksa, Kovacevic, Strahinja Z.
• Format: Journal Article
• Language: English
• Published: Clovelly Taylor & Francis 03.04.2014; Copyright Agency Limited (Distributor)
• Subjects: Accuracy; Artificial neural networks; Calibration; chemometrics; Datasets; DEATH; Electrolytes; FORENSIC SCIENCE; Forensic sciences; Human remains; Least squares; linear least squares; Mathematical analysis; Mathematical models; Neural networks; post-mortem interval; Potassium; Predictions; Regression analysis; Sodium; Vitreous humour
• ISSN: 0045-0618; 1834-562X
• DOI: 10.1080/00450618.2013.825812

The subject of this paper is to determine the post-mortem interval (PMI) using the data obtained by potentiometric measurements of the electrolyte concentrations (potassium and sodium) in human vitreous humour. The data were processed by linear least squares (LLS) and artificial neural network (ANN) procedures. The LLS mathematical models have been developed as calibration models for prediction of the PMI. The quality of the models was validated by the leave one out (LOO) technique and by using an external data set. High agreement between experimental and predicted PMI values indicated the good quality of the derived models. Additionally, we analysed the influence of various factors (the cause of death, sex, differences between electrolyte concentrations in left and right eye) on the accuracy and reliability of obtained PMI. The ANN method was based on 174 forensic cases with different causes of death and known PMI ranging from 3.1-24.1 hours. The external data sets corresponding to 40 selected forensic cases were tested. Excellent correlation between experimental PMI and PMI predicted by ANN was obtained with a coefficient of correlation r 2 =0.9611. In comparison to the LLS regression method applied on the complete available data, the prediction of PMI with ANN was improved by1.66 hours.