Numerical insights into heat, mass, and gas transport during storage of Agaricus bisporus

White button mushrooms ( Agaricus bisporus ) are highly perishable commodities whose postharvest quality rapidly declines due to intensive respiration, moisture loss, enzymatic browning, and microbial proliferation. Ensuring their freshness and extending shelf life remain major challenges in the cold-chain management of fresh produce. In this study, a fully coupled Multiphysics simulation framework was developed in COMSOL Multiphysics to quantitatively predict the postharvest quality evolution of mushrooms under various storage conditions. The model integrated heat and mass transfer, gas exchange, and microbial growth kinetics, coupled with Arrhenius-type quality degradation equations, to represent the dynamic interactions between physical and biological processes during storage. A boundary ordinary differential equation (ODE) was incorporated to estimate the mold growth risk index, providing a comprehensive platform for shelf-life assessment. Simulation results revealed an approximately 20% reduction in the overall quality index (Q) during ten days of storage, consistent with experimental measurements of color parameters (L*, a*, b*), moisture content, and weight loss. The high degree of agreement between predicted and observed data confirmed the robustness and predictive capability of the model. Furthermore, the mold risk index, which increased progressively with time and temperature, effectively captured the transition from safe to critical spoilage conditions. Overall, the developed model demonstrates the potential of Multiphysics-based digital simulation as a non-invasive, predictive, and optimization tool for postharvest systems. It provides valuable insights into the coupled mechanisms of quality degradation and offers a scientific basis for designing packaging strategies, controlling storage environments, and reducing postharvest losses in the mushroom supply chain.