|Course coordinator(s)||dr. ir. JK Heising|
|Lecturer(s)||prof. dr. ir. CGPH Schroën|
|dr. ir. M Dekker|
|dr. ir. JK Heising|
|Examiner(s)||dr. ir. M Dekker|
|prof. dr. ir. CGPH Schroën|
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.
After successful completion of this course students are expected to be able to:
- explain and infer the nature of food quality attributes in relation to food quality;
- classify and infer food quality attribute changes from thermodynamic and kinetic principles;
- practice with the nature and properties of mathematical equations relevant for food quality;
- deduce models, parameters and model predictions and their uncertainties;
- assess competing models on their ability to predict;
- appraise models on food quality that are applied in food science literature.
- class teaching of basic principles;
- practicals consisting of computer exercises in Excel;
- a written exam with multiple choice (33%) and open (67%) end questions counts for 100% in the final result;
- the case studies 1 and 2 are to practice (0%), while case 3 needs a pass to obtain the final course mark.
To pass the course a minimum grade of 5.5 for the written exam is needed and a pass for case study 3 (if one of the parts is insufficient, this part should be done again).
The period of validity of “the pass” for the case-study is set at a maximum of 3 years.
Reader and additional information, such as slides, computer exercises and answers will be provided in Brightspace.