Research of extraction behavior of heavy metal Cd in tea based on backpropagation neural network

In order to meet the increasing demand for food and beverage safety and quality, this study focused on the application of a back propagation (BP) neural network to determine the leaching rate of heavy metal in tea to improve the scientific health of tea drinking. The evaluation index and target expectations have been determined based on the extraction experiment of heavy metal Cd in tea soaking, with 3 evaluation index values taken as input layer parameters and the heavy metal extraction rate taken as output layer parameter. Then, employ the sample data standardized by min‐max linearization method to train and test the network model and get the satisfactory results, which showed that the constructed BP neural network expressed a fast convergence speed and the systematic error was as low as 0.0003509. Additionally, there was no significance between Cd leaching rate of experimental results and neural network model results by reliability testing with a correlation coefficient was .9895. These results revealed that the network model established possessed an outstanding training accuracy and generalization performance, which effectively reflected the extraction rate of heavy metal in tea soaking and improved the safety of tea drinking.