An Early-Warning Risk Signal System for Food Safety Enforcement

Food safety inspections are limited in terms of resources, meaning that only a small proportion of establishments can be inspected. This means that inspections rarely occur based on early indicators. Customer reviews from sites such as Google Maps often include details of hygiene issues including pests, spoilage, temperature and unhygienic conditions. We propose an advisory early-warning system to help identify high-priority establishments to inspect based on customer reviews. The system includes six steps: collecting reviews, identifying hazards in five hygiene categories (foodborne illness, pest control, cross-contamination, food spoilage, and sanitation and premises), weighting reviews based on credibility, applying time decay, adjusting based on region and calculating the final score. In the current prototype, hazards are identified using keywords rather than a trained classifier, and negation handling is not yet implemented. The final 0 to 100 score and the spatial clustering have not been calibrated or validated. Therefore this system should be considered a prototype scoring and prioritisation framework, not a validated predictor of food safety violations. The score is used only to rank establishments, and all cases need to be verified by an inspector.