Optimization of Food Processing Parameters Based on Parametric Models

In this paper, a food processing process optimization model is constructed based on the improved particle swarm algorithm. By adding the chaos mapping strategy to the basic particle swarm algorithm, the optimization efficiency of the particle swarm under the constraints is improved. Combining the multi-objective optimization capability of the NDWPSO algorithm, the process parameter optimization process is constructed to optimize the parameters of food processing processes. The proposed method for process parameter optimization is applied to the vacuum drying process parameter optimization experiment of Jujube to test the effectiveness of particle swarm process parameter optimization. The results show that the diversity value and the best-adapted value of the NDWPSO algorithm in the single-peak function only use a small number of iterations to drop to a level close to 0, indicating that the NDWPSO algorithm has a faster convergence speed. In the vacuum drying process of jujube, the theoretical values of the optimal process parameters for freeze-dried jujube slices are: jujube slices thickness 5mm, sublimation drying temperature -22℃, resolution drying temperature 20℃.