A Review of Localized Food Safety Frameworks for Risk-Based Inspections

Emerging economies face an "enforcement deficit" when aligning with international food safety standards. Modern frameworks rely heavily on quantitative risk-scoring architectures like Multicriteria Decision Analysis (MCDA), which require exhaustive digital infrastructure and laboratory integration. For resource-constrained authorities, importing these data-heavy systems often turns risk assessment into performative compliance rather than functional market surveillance. This study analyzes dominant regulatory models to propose a novel, "lightweight" MCDA framework tailored for emerging economies. By using a targeted Weighted Compliance Score (WCS), this model decouples risk prioritization from exhaustive data demands, shifting the algorithmic focus to easily observable compliance vectors like food labeling and packaging. This approach categorizes regulatory non-conformities into clear risk tiers without needing advanced diagnostic equipment or centralized data silos. Modeled as a proof-of-concept for the Albanian regulatory context, this localized framework provides a highly scalable tool. It enables regulatory bodies to establish objective inspection priorities immediately while progressively building the digital foundations necessary for future integration into global food safety networks.