Prediction of Antioxidant Status in Fish Farmed on Selenium Nanoparticles using Neural Network Regression Algorithm

Abstract

Oxidative stress is the most common stress form which is responsible for the increased mortality as well as retardation of productivity in sheries. Selenium plays a vital role in combating oxidative stress. It appears as a potent antioxidant with reduced toxicity in its nanoscale form. In this paper, the effect of the different concentrations of Nano-selenium in the diet on the antioxidant status of common carp was investigated through the estimation of antioxidant enzymes activity and some biochemical blood prole. The adopted regression algorithm for prediction was Back-propagation Neural Network. The model compromised between fast analytical technologies and biological aspect through prediction the healthy status and expected hazards related to oxidative stress. The experiment was performed on four groups of common carp with measured rearing parameters and the same amount of diet at the rates of 0 (control), 0.5, 1 and 2 mg/k gm amount of Nano-selenium concentration in the ration, aiming to build preliminary prediction models to know the antioxidant status activity. The regression performance was tested by several mathematical validations including MSE (Mean squared error), RMSE (Root mean squared error), MSRE (mean squared relative error), MARE (mean absolute relative error), RMSRE (root mean squared relative error), MAE (Mean absolute error), MAPE (Mean absolute percentage error), MSPE (mean squared percentage error), RMSPE (root mean squared percentage error) which showed promising results of the regression model.