Predictive Food Safety Risk Monitoring

Due to the dynamic nature of the food supply ecosystem and rising reports on food-borne disease outbreaks, effective food safety risk monitoring has become crucial for public health and socioeconomic development. The Singapore Food Agency (SFA) is a statutory board that oversees the food safety and security of Singapore. Part of its functions is to monitor and detect potential food-borne incidents. Currently, SFA scientists rely on manual efforts to gather relevant information from various internal datasets for risk assessments and decision making. This is not only time-consuming but also susceptible to human errors. To tackle the problem, this paper proposes a system called SFA-FSM, which applies advanced AI technologies for predictive food safety risk monitoring. In particular, we fine-tune Transformer-based pre-trained language models such as BERT and XLNet into SFA-FSM for online food safety news monitoring, customer feedback monitoring, and named entity extraction. Moreover, we have conducted experiments to evaluate the proposed food safety monitoring approaches. The experimental results have shown that the proposed approaches have achieved promising performance.