Despite extensive commercial and legislative efforts, foodborne illness caused by pathogenic contamination represents a growing global health concern, with an estimated annual caseload exceeding 600 million and over 420,000 associated deaths. The associated cost of such illnesses continues to weigh heavily on healthcare systems, to the order of billions of dollars each year. The economic effects of food contamination are further exaggerated by the associated food waste and the cost of retroactive food recalls, which are implemented once a contamination event is identified. Alongside pathogenic contamination, food spoilage represents a concurrent concern. While illnesses caused by the consumption of spoiled food products offer significantly better prognoses, the predicted expiry dates used to prevent such events yield significant food waste due to their limited accuracy. With several studies estimating that approximately a third of edible food products are wasted each year, continued societal reliance on static expiry dates has become a significant economic and environmental concern. Fueled by this crisis, the intersection between food science and biosensing has garnered significant interest. Efforts have been made towards the development of real-time, on-package contamination and spoilage detection platforms, which provide a comprehensive solution that would significantly improve food safety. While various detection strategies have been applied in the development of on-package detection platforms, considerations toward commercial viability and ease-of-use have brought focus towards colorimetric and fluorescence transduction systems. Colorimetric systems are optimal from an ease-of-use perspective, but the incorporation of colorimetric systems in situ has proven difficult. Specifically, most colorimetric signaling mechanisms require intricate reaction cascades that rely on numerous reagents. The incorporation of such complex reaction mixes onto food packaging is largely impractical. While a few commercially feasible platforms have been proposed, their real-world applicability is limited by their poor sensitivity and specificity.