Predictive Stacking Model to Link Formulation Design and Mechanical Performance in Coconut-Based Vegan Cheese.

The structural stability of soft foods during storage, transport, and handling is strongly influenced by time-dependent deformation under sustained loading. Although finite element analysis (FEA) has been used to evaluate stresses and displacements in food materials, conventional elastic simulations do not account for the progressive loss of stacking capacity caused by creep. This study proposes a two-tier predictive framework that combines linear static FEA with a modified Burgers viscoelastic model to estimate the instantaneous and time-dependent stacking capacity of soft food materials. A coconut milk-based cheese was considered as a hypothetical application case; however, because this product has not yet been mechanically characterized, the simulations were parameterized using surrogate elastic properties reported for commercial Gouda-type cheeses and literature-derived viscoelastic parameters representative of semihard dairy matrices. Under these surrogate material assumptions, the elastic analysis yielded a maximum von Mises stress of 7.58 × 10-4 MPa, a maximum displacement of 0.016 mm, a safety factor of 13.2, and an instantaneous stacking capacity of approximately 33 units. The modified Burgers model predicted a reduction in capacity to approximately 20 units after 1 h and 15 units after 5 h. Sensitivity analysis identified the instantaneous elastic modulus and Maxwell viscosity as influential parameters within the adopted surrogate parameter set. These stacking capacities should be interpreted as model-based projections for the surrogate material rather than as experimentally validated limits for the coconut-based cheese. The principal contribution is a transferable physics-based framework that can be recalibrated and validated using product-specific mechanical properties.