Fungal Growth Risk Prediction and Optimal Regulation Method for Food Storage Based on the Forward Reachable Set

Affected by coupled environmental factors including temperature and water activity, food storage is restricted by fungal contamination, quality degradation, and energy limits. Conventional microbial growth prediction models typically rely on given initial states and environmental parameters, making it difficult to account for the effects of prior-parameter errors and thereby limiting the accurate quantification of fungal growth risk and the real-time regulation of storage environments. This paper develops a fungal growth risk prediction and optimal regulation method for food storage based on the forward reachable set (FRS). The method combines a fungal growth kinetic model for Aspergillus flavus with FRS theory to calculate the reachable domains of colony radius and cell states within a finite time horizon, adopts a risk margin to describe the maximum colony expansion relative to deterministic growth trajectories, and constructs a multi-objective index covering energy cost, fungal growth risk, quality loss, and control switching cost to select the optimal environmental control scheme. Numerical simulation results show that the risk margin reflects the expansion of fungal growth risk caused by the propagation and accumulation over time of prior-parameter errors, while the selected regulation strategy exhibits stronger conservatism.