Identifiability and robust parameter estimation in food process modeling: Application to a drying model

Model based methods are fundamental in modern food process engineering. The most realistic models combine the physical laws of conservation and constitutive relations associated with kinetic transformations and physical properties, which usually depend on non-measurable parameters. Therefore, a crucial step in model development is model calibration, that is, the computation of those parameters based on experimental data.In this contribution, a two-step approach for proper model calibration is proposed. The first step, usually disregarded, consists of performing a structural identifiability analysis to evaluate the (im-)possibility of giving unique solutions for the model parameters. The second step consists of using robust parameter estimation techniques, based on global optimization methods as the alternative to surmount the convergence to sub-optimal solutions which may lead to wrong conclusions about model predictive capabilities.A typical model for food air-drying is presented as a case study in order to highlight usual difficulties associated with the calibration of food processing models, and how the proposed two-step procedure can help modelers to overcome such difficulties.