Evolution Performance of Multiple Regression and Neural Network Models to Prediction the Activity of Antioxidant Enzymes in Shoots of Wheat (Triticum aestivum) under Cadmium Toxicity

The aim of this study was to compare the performance of multiple regression and neural network models to predict the activity of antioxidant enzymes superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX) and peroxidase (POX) in shoots of wheat (Triticum aestivum), Alvand cultivar in a soil contaminated with cadmium. The treatments consisted of four levels of cadmium (0 (as control), 25, 50 and 100 mg cadmium kg-1 soil), respectively. After 30 days (almost simultaneously with stage of the plant's stem elongation) plant samples were harvested. The following ten different parameters measured included wet and dry weight, chlorophyll a and b, Concentrations of cadmium, copper, iron, manganese, zinc and potassium, respectively. The amount of enzyme activity of SOD, CAT, APX and POX were measured. As next stage, the correlation coefficients between ten parameters and the activity of antioxidant enzymes were determined. The results of multiple regression and neural network models optimized showed that the efficiency of artificial neural network in predicting the activity of SOD and POX enzymes was more of than multiple regression models. Coefficient of multiple determinations (r2) between measured and predicted values of SOD activity for multiple regression and neural network models was 0.76 and was 0.87 respectively. Coefficient of multiple determinations (r2) of POX activity for multiple regression and neural network models was 0.96 and was 0.98 respectively. Coefficient of multiple determinations (r2) between measured and predicted values of CAT activity for multiple regression and neural network models was 0.97 and was 0.98 respectively. In regarding the APX enzyme, coefficients for multiple regression and neural network models was 0.97 and 0.99 respectively. According to the results of this research, generally efficiency of artificial neural network model in predicting the activity of antioxidant enzymes of wheat shoots, under toxicity of Cd was more than the multivariate regression model.