1932

Abstract

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.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-food-022811-101247
2013-02-28
2024-05-23
Loading full text...

Full text loading...

/content/journals/10.1146/annurev-food-022811-101247
Loading
/content/journals/10.1146/annurev-food-022811-101247
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error