The course aims to provide students with the knowledge related to the most used modeling and optimization methods in the food industry. The course also aims to provide students with the knowledge related to innovative technologies their applications.
At the end of the course, students will be able to construct, through the use of dedicated software, statistical models capable of describing a production process to optimize operating conditions and predict the properties of the finished product. Students will also be able to evaluate the potential applications in the various food processes of the most innovative technologies.
1. Introduction to process modeling. Comparison between the fundamental and the empirical approach. Principles, definitions, aims, examples of application. 2. The different statistical methods used for model building: linear, non linear and weighted regressions. The least square method to estimate model parameters. 3. Principles, aims and procedures of experimental design. The choosing criteria among the different experimental designs. The analysis of variance. The one factor comparative design and the use of blocking. The two-level full and fractional factorial designs. The principles of confounding and design resolution. Methods to improve the resolution of fractional design. The Plackett-Burman designs. The addition of centre points to factorial design. 4. The response surface designs: Central Composite and Box-Behnken designs. 5. The optimization procedures for single or multiple response. The steepest ascent method. The desirability function. 6. The mixture designs: simplex lattice and simplex centroids. Combining mixture and process factors: D-optimal design. 7. Thermal and non thermal novel food processing. High pressure, pulsed electric field, microwave, and ohmic heating processes. - Computer practice for the modeling and optimization of food process and products by applying the different design methods. Softwares: Excel and Design Expert.