The simultaneous diffusion of inorganic components in the olive pulp in wet brine was modeled based on Fick’s generalized 2nd Law and simulated using the finite element method. The main and crossed diffusion coefficients, the film coefficient and the Biot number were determined, with the application of the simplex optimization method, through the minimization of the percentage errors. The errors between the simulated and experimental data were 5.35% for NaCl and 4.77% for KCl and the adjusted main diffusion coefficients were 0.4358 × 10-12 m2 s-1 for NaCl and 0.5408 × 10-12 m2 s-1 for KCl. The system developed to simulate diffusion allows the control and modulation of the salts content that diffuses through the olive pulp.

Olive samples were subjected to the salting process in brine containing 1196 mol m−3 of NaCl and 402 mol m−3 of KCl. Samples were collected during 60 h and salt concentration values were determined. With the finite element method (FEM) and the minimization of the errors percentage between the simulated and experimental values, the mass transfer coefficients in the film (hm) were obtained, being 1.0072 × 10−8 and 1.2499 × 10−8 for NaCl and KCl, respectively. The salts concentration at the olive/brine interface was simulated by FEM and analyzed via SOFM. A network with 4 × 4 topology and 10000 training epochs was used. It was observed that the influence of the stationary film formed on the surface of the olives during the salting process depends on the position, the salt involved in the diffusion and that the concentration of the salts, at each point, varies according to the immersion time.

Sodium chloride is used in the cheese salting process as it promotes sensory changes and food preservation. However, in excess can cause hypertension problems, and for this reason, it has been partially replaced by potassium chloride. In the present work, Prato cheese was subjected to joint diffusion of NaCl and KCl by immersion in static and stirred brine. The salt concentration values on the cheese surface were determined as a function of time. They were tabulated and presented to the self-organizing map (SOM)-type neural networks and the multilayer perceptron (MLP) for analysis and modeling of the film formed on the surface of the cheese during static and dynamic salting. The SOM network showed that the behavior of the diffusive process, on the surface of the cheese, depends on the analyzed position and that the points in which occupy similar positions, concerning the distance from the edge, have similar behavior. Regression models for NaCl and KCl were significant at the 5% level and can be used for predictive purposes.

The influence of the film formed during the salting of champignon mushrooms with brine containing NaCl and KCl was modeled using the finite elements method (FEM). It was verified that the film formed on the mushroom surface had a greater influence in the static salting since the diffusion of the ions was 7.5-fold smaller in this system than in the stirred salting. The application of self-organizing maps showed that the ions diffusion along the surface of the solid presented a heterogeneous occurrence and depended on the region for both static and stirred salting. A direct relation was observed among the mushroom surface morphology, the salts diffusion behavior, and the film formation. In addition, the film was not completely extinguished in the stirred system, although it has a minimal influence as the film formation is also dependent on the biosolid surface.

The NaCl and KCl diffusion in the film formed on the cheese surface during salting was simulated by the finite element method. The time and salts concentration values on the cheese surface were determined, tabulated, and presented to the multilayer perceptron neural network (MLP) for the regression modeling. The samples were divided into 70, 15 and 15% for training, testing, and validation, respectively. The networks with the best performance showed 5 to 12 hidden layers. The Tukey’s test showed that there was no significant difference, at the 5% level, between the time value used and the mean value modeled for training, testing, and validation for the NaCl. For the KCl, a significant difference was observed only for 2 training samples and 1 test sample. Sensitivity analysis showed that the discrete variable Z, which represents the static and dynamic systems, was the most important in the models’ construction.