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

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)...

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Bibliographic Details
Published in:Australian journal of forensic sciences 2014-04, Vol.46 (2), p.166-179
Main Authors: Gadzuric, Slobodan B., Podunavac Kuzmanovic, Sanja O., Jokic, Aleksandar I., Vranes, Milan B., Ajdukovic, Niksa, Kovacevic, Strahinja Z.
Format: Article
Language:English
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
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Summary: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.
ISSN:0045-0618
1834-562X
DOI:10.1080/00450618.2013.825812