Prediction of copper and chromium concentrations in bean leaves based on an artificial neural network model

The assessment of copper and chromium concentrations in plants requires the quantification of a large number of soil factors that affect their potential availability and subsequent toxicity and a mathematical model that predicts their relative concentrations in plants. While many soil characteristics have been implicated as altering copper and chromium availability to plants in soil, accurate, rapid and simple predictive models of metal concentrations are still lacking for soil and plant analysis. In the current study, an artificial neural network model was developed and applied to predict the exposure of bean leaves (BL) to high concentrations of copper and chromium versus some selected soil properties (pH, soil electrical conductivity and dissolved organic carbon). A series of measurements was performed on soil samples to assess the variation of copper and chromium concentrations in BL versus the soil inputs. The performance of the artificial neural network model was then evaluated using a test data set and applied to predict the exposure of the BL to the metal concentration versus the soil inputs. Correlation coefficients of 0.99981 and 0.9979 for Cu and 0.99979 and 0.9975 for Cr between the measured and artificial neural networks predicted values were found, respectively, during the testing and validation procedures. Results showed that the artificial neural network model can be successfully applied to the rapid and accurate prediction of copper and chromium concentrations in BL.