Bayesian risk prediction model: An accessible strategy to predict cadmium contamination risk in wheat grain grown in alkaline soils.

ABSTRACT

Excessive cadmium (Cd) concentration in wheat grain is becoming a widespread concern in China. Considering the complexity of Cd transfer in the soil-wheat system, how the Cd risk in wheat grain be accurately predicted from the limited details available is of great significance for the risk management of Cd. Bayes' theory could leverage existing data by combining prior information and observational data, providing a promising strategy with which to calculate a more robust posterior probability of a grain sample exceeding the food safety standard (FSS) for Cd (0.1 mg kg-1). In the current study, a risk prediction model, based on Bayes' theory, was established to achieve a more accurate prediction of the wheat grain Cd risk from a limited number of soil parameters. The risk prediction model could predict the risk probability of wheat grain with a Cd concentration exceeding the FSS under a given soil concentration of either total Cd or diethylenetriaminepentaacetic acid (DTPA)-extractable Cd. Soil total Cd concentration proved to be a better variable for the model with greater predictive accuracy. The model predicted that fewer than 5% of the wheat grain would have a Cd concentration exceeding the FSS when grown in soil with a total Cd concentration of less than 0.299 mg kg-1. The risk probability rose significantly to 50% when the soil total Cd reached 0.778 mg kg-1. The accuracy of the model was greater than the widely applied multiple linear regression model, whereas previously published data from similar soil conditions also confirmed that the Bayesian model could predict wheat Cd risk with minimal error. The proposed model provides an accurate, accessible and cost-effective methodology for predicting Cd risk in wheat grown in alkaline soils before harvest. The wider application to other soil conditions, crops or contaminants using the Bayesian model is also promising for risk management authorities.