Increasing global and domestic food trade and required logistics create uncertainties in food safety inspection due to uncertainties in food origins and extensive trade activities. Modern blockchain techniques have been developed to inform consumers of food origins but do not provide food safety information in many cases. A novel food safety tracking and modeling framework for quantifying toxic chemical levels in the food and the food origins was developed. By integrating chemicals' multimedia environment exchange, food web, and source tracking systems, the framework was implemented to identify short-chain chlorinated paraffin (SCCP) contamination of fresh hairtail fish sold by a Walmart supermarket in Xi'an, northwestern China, and sourced in Eastern China Sea coastal waters. The framework was shown to successfully predict SCCP level with a mean of 17.8 ng g-1 in Walmart-sold hairtails, which was comparable to lab-analyzed 21.9 ng g-1 in Walmart-sold hairtails. The framework provides an alternative and cost-effective approach for safe food inspection compared to traditional food safety inspection techniques. These encouraging results suggest that the approach and rationale reported here could add additional information to the food origin tracking system to enhance transparency and consumers' confidence in the traded food they consumed.
Keywords: SCCP; Walmart; exposure risk; food origins and safety; food trade and logistics.