Modeling Food Processing Operations
Computational modeling is an important tool for understanding and improving food processing and manufacturing. It is used for many different purposes, including process design and process optimization. However, modeling goes beyond the process and can include applications to understand and optimize...
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| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Burlington :
Elsevier Science
2015.
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| Colección: | Woodhead Publishing Series in Food Science, Technology and Nutrition
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009633561206719 |
Tabla de Contenidos:
- Front Cover; Modeling Food Processing Operations; Copyright; Contents; List of contributors; Woodhead Publishing Series in Food Science, Technology and Nutrition; Preface; Part One: Introduction to computational modeling in food processing; Chapter 1: Different modelling and simulation approaches for food processing operations; 1.1. Introduction and intended contribution; 1.2. Basic considerations of food processing; 1.3. Modelling and simulation approaches; 1.3.1. Balancing approaches; 1.3.1.1. Balancing approaches for thermal processes considering pure time dependence
- 1.3.1.2. Balancing approaches including spatiotemporal effects1.3.2. Knowledge-oriented approaches; 1.3.3. Hybrid approaches; 1.4. Conclusions and outlook; References; Part Two: Modeling of food processes involving heating and cooling; Chapter 2: Thermal processing and kinetic modeling of inactivation; 2.1. Introduction; 2.1.1. Thermal treatment in food processing; 2.1.1.1. Beneficial features of the thermal processing of foods; Effect on digestibility and quality properties; Control of enzymes and microorganisms; 2.1.1.2. Undesired effects of thermal processing
- 2.2. Quality and microbial modeling during thermal processes2.2.1. Selection of quality and microbial indices for quantitative and kinetic assessment of thermal processes; 2.2.2. Current modeling practices: Parameter estimation under isothermal conditions; 2.3. Dynamic temperature parameter estimation for microbial inactivation; 2.3.1. Parameter estimation using ordinary least squares; 2.3.1.1. Ordinary least squares for linear models (Beck and Arnold, 2007); 2.3.1.2. Ordinary least squares for nonlinear models (Beck and Arnold, 2007); 2.3.1.3. Statistics for the parameters
- Statistics for the parameters2.3.1.4. Parameter estimation best practices; Avoidance of linear dependence; Minimization of parameter errors; Scaled sensitivity coefficients; 2.3.2. Parameter estimation using the sequential procedure; 2.4. Model selection for dynamic parameter estimation; 2.4.1. Residual analysis; 2.4.2. Akaike criterion corrected; 2.4.3. Sequential estimation; 2.4.4. Example; 2.5. Software programs dealing with dynamic forward and inverse modeling problems in food science; 2.6. Future trends; References; Chapter 3: Modeling thermal processing and reactions; 3.1. Introduction
- 3.1.1. Brief history3.1.2. Commercial sterilization fundamentals; 3.2. Heat transfer; 3.2.1. Heat transfer in thermal processing; 3.2.2. Mathematical modeling and its implications for process evaluation techniques; 3.2.2.1. Heat transfer model for perfect mixing; 3.2.2.2. Heat transfer model for pure conduction; 3.2.2.3. Heat transfer model: a general approach; 3.2.2.4. Simulation of the thermal processing of nonsymmetric and irregular-shaped foods: a numerical example; Reverse engineering by 3D digitizing; Simulation of heat conduction processes; Finite element analysis
- Experimental validation
