Modelling in Food Engineering

ECTS points:
3

Program:
diplomski

Course number:
53291

Course Description

COURSE CONTENT

Mathematical modeling and its application (and importance) in food engineering.
How to evaluate the application of modeling and chemometric techniques in the processing of experimental data
The organization method of data analysis according to the complexity of the (descriptive analysis and multivariate analysis)
The way you design complex data analysis according to the set objectives of the research, using chemometric tools (cluster analysis, factor analysis and principal component analysis)
Interpretation and valid conclusions in the observed multivariate system using specific computer skills in the available computer programs

The topics are as follows:

Mathematical models and their basics.

Models through the manufacturing system in the food industry.

Basics of Data Analysis and Computer Support Overview Determining the Space of Major Components and Latent Variables. Identification and classification of food samples in the space of the main components. Applying regression models for monitoring and management. Estimation of space by chemometric method. Process quality algorithms based on "cluster analysis" in the main components area.

Seminar presentation (S = 2)

Individual seminar work with the topic modeling using processes and collected data from a chosen food production process or a part of it.

LEARNING OUTCOMES

  • define mathematical modeling and its application (and importance) in food engineering
  • identify primary and secondary "variables" in the observed system with the use of technological processes models
  • evaluate the application of modeling and chemometric techniques in processing experimental data
  • organize data analysis methods by complexity (descriptive analysis and multivariate analysis)
  • plan complex data analysis according to the set research goals, using the chemometric tools (cluster analysis, factor analysis and main component analysis)
  • create and evaluate conclusions about the connection of variables and samples in the observed multivariate system using certain computer skills (Excel. XLStat, R program)