A deep network prediction model for heavy metal cadmium in the rice supply chain

Abstract

Cadmium and its compounds are currently known as Class I carcinogens, and excessive intake can cause severe health damage to humans. Rice has a strong absorption effect on cadmium, and rice products with excessive cadmium content have caused several significant public health contamination incidents. It is essential to predict the development trend of cadmium hazards in the rice supply chain so that countermeasures can be formulated to reduce the hazards. This paper proposes a deep prediction model for cadmium hazards in the rice supply chain based on the regularization method. Firstly, a long and short-term memory network is used to build the depth prediction model by using the regularization method, and the noise penalty term is added to reduce the model fitting to the noise and prevent the over-fitting caused by the noise. Finally, the optimization of the model hyperparameters was carried out using a Bayesian optimization approach to develop the prediction performance. Then, early warning system for prediction of cadmium hazards in the rice supply chain is built based on the deep prediction model proposed in this paper with SOA architecture, including data resource, business logic, and application service layers. The proposed model performs well on an actual data set of cadmium hazards in the rice supply chain and fits the data well.

Keywords

• Rice supply chain
• Cadmium
• Predictive models
• Long and short term memory
• Bayesian optimization