Parameter estimation in food science.
Annu Rev Food Sci Technol
; 4: 401-22, 2013.
Article
en En
| MEDLINE
| ID: mdl-23297775
Modeling includes two distinct parts, the forward problem and the inverse problem. The forward problem-computing y(t) given known parameters-has received much attention, especially with the explosion of commercial simulation software. What is rarely made clear is that the forward results can be no better than the accuracy of the parameters. Therefore, the inverse problem-estimation of parameters given measured y(t)-is at least as important as the forward problem. However, in the food science literature there has been little attention paid to the accuracy of parameters. The purpose of this article is to summarize the state of the art of parameter estimation in food science, to review some of the common food science models used for parameter estimation (for microbial inactivation and growth, thermal properties, and kinetics), and to suggest a generic method to standardize parameter estimation, thereby making research results more useful. Scaled sensitivity coefficients are introduced and shown to be important in parameter identifiability. Sequential estimation and optimal experimental design are also reviewed as powerful parameter estimation methods that are beginning to be used in the food science literature.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Tecnología de Alimentos
/
Modelos Teóricos
Idioma:
En
Revista:
Annu Rev Food Sci Technol
Año:
2013
Tipo del documento:
Article
País de afiliación:
Estados Unidos
Pais de publicación:
Estados Unidos