Dynamic Modelling and Simulation of Food Systems
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 consu...
Saved in:
Other Authors: | , , |
---|---|
Format: | Electronic Book Chapter |
Language: | English |
Published: |
Basel
MDPI - Multidisciplinary Digital Publishing Institute
2023
|
Subjects: | |
Online Access: | DOAB: download the publication DOAB: description of the publication |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
MARC
LEADER | 00000naaaa2200000uu 4500 | ||
---|---|---|---|
001 | doab_20_500_12854_98124 | ||
005 | 20230307 | ||
003 | oapen | ||
006 | m o d | ||
007 | cr|mn|---annan | ||
008 | 20230307s2023 xx |||||o ||| 0|eng d | ||
020 | |a books978-3-0365-6693-1 | ||
020 | |a 9783036566924 | ||
020 | |a 9783036566931 | ||
040 | |a oapen |c oapen | ||
024 | 7 | |a 10.3390/books978-3-0365-6693-1 |c doi | |
041 | 0 | |a eng | |
042 | |a dc | ||
072 | 7 | |a GP |2 bicssc | |
072 | 7 | |a KCN |2 bicssc | |
100 | 1 | |a Vilas, Carlos |4 edt | |
700 | 1 | |a García, Míriam R. |4 edt | |
700 | 1 | |a Egea, Jose A. |4 edt | |
700 | 1 | |a Vilas, Carlos |4 oth | |
700 | 1 | |a García, Míriam R. |4 oth | |
700 | 1 | |a Egea, Jose A. |4 oth | |
245 | 1 | 0 | |a Dynamic Modelling and Simulation of Food Systems |
260 | |a Basel |b MDPI - Multidisciplinary Digital Publishing Institute |c 2023 | ||
300 | |a 1 electronic resource (252 p.) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |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. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a Research & information: general |2 bicssc | |
650 | 7 | |a Environmental economics |2 bicssc | |
653 | |a food safety | ||
653 | |a predictive microbiology | ||
653 | |a mathematical models | ||
653 | |a microbial inactivation | ||
653 | |a sublethal injury | ||
653 | |a bioprocess engineering | ||
653 | |a fermentation process | ||
653 | |a batch bioreactors | ||
653 | |a dynamical non-linear mathematical model | ||
653 | |a model identification | ||
653 | |a particle swarm optimization | ||
653 | |a simulation | ||
653 | |a Carnobacterium maltaromaticum | ||
653 | |a modeling | ||
653 | |a microbial growth | ||
653 | |a optimization | ||
653 | |a fermentation | ||
653 | |a temperature-dependent thermal properties | ||
653 | |a scaled sensitivity coefficient | ||
653 | |a TPCell | ||
653 | |a parameter estimation | ||
653 | |a inverse problems | ||
653 | |a food microstructure | ||
653 | |a electronic nose | ||
653 | |a Shewanella putrefaciens | ||
653 | |a dynamic growth | ||
653 | |a spoilage prediction | ||
653 | |a GC-MS | ||
653 | |a acrylamide formation | ||
653 | |a thermal resistance | ||
653 | |a dynamic models | ||
653 | |a FSSP | ||
653 | |a DoE | ||
653 | |a smoke | ||
653 | |a fish | ||
653 | |a wine fermentation | ||
653 | |a nitrogen | ||
653 | |a mathematical modeling | ||
653 | |a population model | ||
653 | |a maintenance | ||
653 | |a variable yield | ||
653 | |a underutilized wild species | ||
653 | |a lycopene | ||
653 | |a viscosity | ||
653 | |a thermal processing | ||
653 | |a color | ||
653 | |a mathematical modelling | ||
653 | |a fish quality | ||
653 | |a fish freshness | ||
653 | |a bibliometric analysis | ||
653 | |a stress variables | ||
653 | |a quality degradation | ||
653 | |a beer fermentation | ||
653 | |a food industry | ||
653 | |a multi-objective optimization | ||
653 | |a model-based optimization | ||
653 | |a equivalent solutions | ||
653 | |a uncertainty | ||
653 | |a Monte Carlo | ||
653 | |a frying operation | ||
653 | |a acrylamide | ||
653 | |a quality | ||
653 | |a n/a | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/6897 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/98124 |7 0 |z DOAB: description of the publication |