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230515 ||| eng |
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|a 9783036566924
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|a books978-3-0365-6693-1
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|a 9783036566931
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| 100 |
1 |
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|a Vilas, Carlos
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| 245 |
0 |
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|a Dynamic Modelling and Simulation of Food Systems
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (252 p.)
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| 653 |
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|a particle swarm optimization
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| 653 |
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|a thermal resistance
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| 653 |
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|a fermentation
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| 653 |
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|a underutilized wild species
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|a mathematical models
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| 653 |
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|a food industry
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| 653 |
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|a color
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|a maintenance
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|a fish
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|a fermentation process
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|a n/a
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|a Carnobacterium maltaromaticum
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|a acrylamide
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|a stress variables
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|a food microstructure
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|a frying operation
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|a uncertainty
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|a DoE
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|a Monte Carlo
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| 653 |
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|a nitrogen
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| 653 |
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|a model identification
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| 653 |
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|a GC-MS
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|a simulation
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| 653 |
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|a food safety
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| 653 |
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|a batch bioreactors
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| 653 |
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|a fish quality
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| 653 |
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|a mathematical modelling
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|a viscosity
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|a population model
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|a lycopene
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|a modeling
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|a bibliometric analysis
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| 653 |
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|a mathematical modeling
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| 653 |
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|a microbial growth
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| 653 |
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|a multi-objective optimization
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|a bioprocess engineering
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|a parameter estimation
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|a wine fermentation
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| 653 |
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|a Research & information: general / bicssc
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|a sublethal injury
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| 653 |
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|a inverse problems
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| 653 |
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|a variable yield
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| 653 |
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|a temperature-dependent thermal properties
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| 653 |
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|a microbial inactivation
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| 653 |
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|a quality
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| 653 |
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|a predictive microbiology
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| 653 |
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|a thermal processing
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|a scaled sensitivity coefficient
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|a electronic nose
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| 653 |
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|a model-based optimization
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| 653 |
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|a spoilage prediction
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| 653 |
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|a Environmental economics / bicssc
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| 653 |
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|a dynamic growth
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| 653 |
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|a fish freshness
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| 653 |
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|a Shewanella putrefaciens
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| 653 |
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|a beer fermentation
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| 653 |
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|a quality degradation
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|a smoke
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| 653 |
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|a equivalent solutions
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| 653 |
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|a acrylamide formation
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| 653 |
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|a TPCell
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| 653 |
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|a FSSP
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| 653 |
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|a dynamical non-linear mathematical model
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| 653 |
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|a optimization
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| 653 |
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|a dynamic models
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| 700 |
1 |
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|a García, Míriam R.
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| 700 |
1 |
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|a Egea, Jose A.
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| 700 |
1 |
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|a Vilas, Carlos
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 024 |
8 |
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|a 10.3390/books978-3-0365-6693-1
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| 856 |
4 |
0 |
|u https://directory.doabooks.org/handle/20.500.12854/98124
|3 Volltext
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|a 363
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|a 000
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|a 576
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|a 500
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|a 700
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|a 620
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|a 330
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|a Several factors influence consumers’ choices of food products. While price remains the main criterion, quality, pleasure, convenience, and health are also important driving factors in food market evolution. Food enterprises are making significant efforts to manufacture products that meet consumers’ demands without compromising on safety standards. Additionally, the food industry also aims to improve the efficiency of transformation and conservation processes by minimizing energy consumption, process duration, and waste generation. However, foods are highly complex systems in which: (i) Non-linear dynamics and interactions among different temporal and spatial scales must be considered; (ii) A wide range of physical phenomena occur; (iii) Different food matrices, with different microstructures and properties are involved; and (iv) The number of quality and safety indicators (such as bacteria, total volatile basic nitrogen, color, texture, odor, and sensory characteristics) is substantial. Mathematical modeling and simulation are key elements that allow us to gain a deeper understanding of food processes and enable the use of tools such as optimization and real-time control to improve their efficiency. This Special Issue gathers research on the development of dynamic mathematical models that describe the relevant factors in food processes, and model-based tools to improve such processes. The contributions published in this Special Issue can be grouped into two categories: the evolution of safety and quality indicators in unprocessed food systems, and transformation and preservation processes.
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