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Summary Module 8 - Predicting Food Quality (31306)

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I have summarized the contents of the knowledge clips, lecture notes and feedback lecture of module 8. This document will help you prepare for the exam. Good luck!

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  • 19 april 2023
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  • 2022/2023
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Module 8:

 If you want to predict food quality, you have to:
o Know how food quality can be described in technological terms via quality
attributes.
o Capture your knowledge into mathematical models.
o Determine the parameters in the models from experiments.
o Establish the uncertainty in the parameters and models.

 We distinguish 2 types of models:
o Empirical models.
o Mechanistic models.

Empirical models:
 For these models, there is no underlying physical theory  these models just give a
mathematical description (e.g., polynomials, rational functions, formal kinetics, etc.).
 The resulting parameters have no physical meaning.
 It is impossible to extrapolate outside the range for which the relationship was
established.

Mechanistic models:
 These models are based on an underlying physical theory.
 Usually expressed as time or space dependent equations, relating a measurable
response to a controllable variable.
 The parameters have a physical meaning (e.g., activation energy, diffusion
coefficient, reaction rate constant, permeability, etc.).

 How to judge a model?
1. Look at the data  plot the data and model prediction + compare.
2. Evaluate the standard deviations and correlations of the parameters.
3. Evaluate the residual plots.

 If you have built different models to describe your data, you can use the Akaike
criterion to compare the models.
 You have to use the corrected Akaike criterion when n/p <40.
o AIC > 2-3  the model with the lowest AIC is the best model. However, the
difference is too insignificant to discard any model.
o AIC > 10  the model with the lowest AIC should be discarded.

 Science is a specific way to look at the world around us. ‘Specific’ for the following
reasons:
- Reproducibility: everyone should be able to reproduce your results.
- Hypothesis/theory: presented to explain the results.
- Criticism: the interpretation of the results is open for debate, and it is allowed to
disagree as long as arguments are given.
 Because of this debate, there is development/progress in science.
 Models are a scientific way to convey a description of the world around us.

, o The danger is that we come to the wrong conclusions, because we do not have
a complete view of the system  i.e., we have not incorporated all existing
information that may affect the data.

 Statistics forms an indispensable part of science. Statistics helps in:
o Interpreting data  separate the noise from relevant information. Note that
measurements always contain noise.
o Designing experiments  how to obtain the most out of data with the least
possible effort.
o Model building  adds the stochastic part.
o Making realistic predictions by making variation explicit.

 Benefits of modeling:
(+) Gives insights into what is happening, aids in scientific understanding, serves as a
tool for research.
(+) Aids in predicting product properties, forming what-if analyses.
(+) Useful for product and process design  demands on food supply change so
rapidly that it is impossible to study everything experimentally.
(+) Aids in controlling food quality and food safety in an efficient way.

 Models in food science literature are often empirical and not very quantitative. These
models are also not that innovative  poor use of the available computing power.
 The limitations and possibilities for kinetic modeling are not very clearly stated.
 Statistics is not always used properly in food science literature, if used at all!

 Note that there is a big difference between models and the truth  models only exist
in our mind, not in nature. Models are solely an approximation of reality. You learn
about reality by:
o Doing experiments.
o Making observations.
 We do not model collected data, but rather the information contained within these
data. Models are thus a summary of information represented by data  they give a
grip on reality.

 In the plot on the right, you see the blue line that
represents reality. Three different models are built
to capture this reality.
 However, the problem is that we do not know this
blue line (reality). If we did, why would we even
need models?
 ‘Something’ in reality causes the data we observe.
This is where science comes in  we try to model
this ‘something’ in reality in terms of chemistry,
physics, etc.
 We can thus propose models, based on scientific insights.
 There are always more models feasible for a certain problem.

 Models must be confronted with reality (i.e., data).

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