FQD-31306 Predicting Food Quality


Credits 6.00

Teaching methodContact hours
Independent study
Course coordinator(s)dr. ir. JK Heising
Lecturer(s)prof. dr. ir. CGPH Schroën
dr. ir. JK Heising
dr. ir. M Dekker
Examiner(s)prof. dr. ir. CGPH Schroën
prof. dr. ir. MAJS van Boekel

Language of instruction:


Assumed knowledge on:

Introduction to Statistics, Food Science and Technology courses (Food Chemistry, Food Microbiology, Food Physics, Food Process Engineering).


The course starts with a general outline of food quality as an important topic for consumers, and therefore for the food industry, and the need to describe this quantitatively will be discussed. For a quantitative analysis, food quality needs to be decomposed in measurable and quantifiable food quality attributes. The course then continues with a general introduction to modelling, addressing how models work, what their opportunities and limitations are, what model parameters are, and what the role of statistics is, namely to estimate model parameters and their uncertainties. This is followed by a lecture on thermodynamics of what drives reactions in foods, as a tool to predict whether or not reactions are possible in foods. If reactions are possible, the next question is whether they also take place at a measurable range. This is the domain of kinetics: how fast do such reactions occur. In between, many examples will be given taken from food science literature and students will actively do modelling on the computer using Excel. The principles are illustrated for chemical, biochemical, physical and microbiological reactions in foods. Applications are shown in shelf life modelling. Moreover, attention will be paid to modelling of reactions in reactors. The course will finish with a reflection on the possibilities and limitations of modelling food quality.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- understand the nature of food quality attributes in relation to food quality;
- understand, apply and analyse model equations;
- infer uncertainties in models, parameters and model predictions;
- evaluate and compare competing models;
- analyse and evaluate models that are applied in food science literature.


- class teaching of basic principles;
- practicals consisting of computer exercises in Excel;
- case-studies.


- written exam with open end questions counting for 75% in the final result;
- case 1 is to practice and does not count, case 2 counts for 5%, case 3 for 20% in the final result.
For each part, written exam and case studies, the minimum grade is set at 5.5 (if one of the parts is below 5.5, this part should be done again).


Reader and additional information, such as slides, computer exercises and answers will be provided via Blackboard.

Compulsory for: MFTFood TechnologyMScC: Product Design5MO
Restricted Optional for: MFTFood TechnologyMScB: Food Innovation and Management5MO
MFTFood TechnologyMScE: Dairy Science and Technology5MO
MFTFood TechnologyMScD: Ingredient Functionality5MO
MFTFood TechnologyMScH: Sustainable Food Process Engineering5MO