MODELING IN FOOD TECHNOLOGY

Models in food technology, at the simplest level, are equations showing the relationship between two or more variables.  Dunn (1986) defined a mathematical model of a process as a 'system of equations whose solution, given specified input data, is representative of the response to a corresponding set of inputs'. 

  The two major types of models used in food technology are empirical and fundamental.

 Most models used in food technology are empirical.  There are two types of empirical model, that is, response surface methodology (RSM) and dimensional analysis.  RSM is a graphical representation of the statistical relationship between process output and independent variables, whilst dimensional analysis is a technique, which combines physical parameters that describe the problem in such a way to produce new, dimensionless, variables, and interactions (Levine, 1997).  The Van Slyke yield equation, for calculating the theoretical yield of Cheddar cheese, is an example of a RSM type model. See the discussion area for questions and answers on the Van Slyke equation.

Fundamental models, or models based on theory, require knowledge of the underlying principles and mechanisms and the relationships between variables.

Advantages of mathematical modeling

• Modeling enables product or process knowledge to be expressed in simple statements thus reducing the apparent complexity of some problems and facilitating a solution
• Has the potential to reduce the cost of experimentation, by reducing the number of experiments needed to analyse a particular problem.  E.g. use of factorial experimental designs
• Models can be extrapolated to unexplored or un-explorable regions. You can try this out with the Cheddar cheese grade prediction model.
• Allows alternatives to be considered which may be difficult, or expensive to test.
• Enables the sensitivity of a process to variables, and the design of optimal control strategies, to be studied.

The above statements have been adapted from Levine (1997).

Modeling in the food industry

The use of modeling in the prediction of cheese yield and quality is discussed in the sections on cheese yield and cheese quality. See also the sections on modeling Cheese grade prediction values and why one cheese is different than another.

Click here to review the Power Point presentation on modeling. 

  Use of models in problem solving

 Problem solving is something that the technologist does every day in industry.  We are going to explore what is involved in problem solving by studying problems that the author has investigated.

 Essentially the approach that we will take is to:

1. Define, explain and move towards actually understanding what the problem is.
2. Based on this understanding, and your knowledge of food technology, including any relevant product or process models you will devise a plan to investigate the causes of the problem.
3. Implement your plan
4. Review your findings to determine whether you have arrived at a solution.

This may be a cyclical process, where you progress to a solution in a logical way by eliminating a number of potential solutions.

  Questions that you might ask

• Are there any product or process models that might be relevant?
• Has this problem occurred before?  If yes, was it investigated?  What was the outcome?  Where are the records?
• Do you how anyone who might have experience of this problem?
• What do the operatives, supervisors, and managers have to say?
• Look at the data.  Are there any trends?
• How do you know that the records/ analyses/ responses to questions are accurate?  Could the responses or results be incorrect, invented?
• Have there been any personnel changes/ problems?
• Is the crisis management plan relevant?

© Copyright February 2001. All rights reserved. W M A Mullan.
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