| Department of Food Science | |||||||||||||||
| Earliest Possible Year | BSc. 3 year to MSc. 2 year | ||||||||||||||
| Duration | One block | ||||||||||||||
| Credits | 7.5 (ECTS) | ||||||||||||||
| Course Level | Joint BSc and MSc | ||||||||||||||
| Examination | Final Examination written examination and oral examination All aids allowed Description of Examination: The students will handle in a written report in due time before the oral examination. At the oral examination the students will be examined in the report as well as the examination requirements. Weight: Written report: 80 % Oral examination: 20 % 13-point scale, internal examiner | ||||||||||||||
| Organisation of Teaching | Forelæsninger, øvelser og projektarbejde udgør hver cirka 1/3 af undervisningen. | ||||||||||||||
| Block Placement | Block 1 Week Structure: B | ||||||||||||||
| Teaching Language | English | ||||||||||||||
| Restrictions | 50 | ||||||||||||||
| Areas of Competence the Course Will Address | |||||||||||||||
| Competences within applied science: The student will obtain knowledge of and understand the basic chemometric methods for multivariate data analysis and understand their use on real life data sets. The student will be able to apply the taught principles on similar data analytical problems. The student will also obtain knowledge of how to apply user-friendly software for multivariate data analysis of real data sets. Competences within ethics and values: The student will be aware of and respond to univariate versus multivariate data analytical methodology in problem solving. | |||||||||||||||
| Course Objectives | |||||||||||||||
| The course introduces basic chemometric methods (PCA, PLS, D-PLS and SIMCA) and their use on different kinds of multivariate data of relevance for research and development. Furthermore, the exploratory element in research and development is illustrated. | |||||||||||||||
| Course Contents | |||||||||||||||
| In industry and research huge amounts of physical, chemical, sensory and other quality measurements are produced on all sorts of materials, processes and products. Exploratory data analysis / chemometrics offers a tool for extracting the optimal information from these data sets through the use of modern software and computer technology. The course will give a step-by-step theoretical introduction to exploratory data analysis / chemometrics supported by practical examples from food science, agro technology, medicine, pharmaceutical science etc. Methods for exploratory analysis (Principal Component Analysis), classification (SIMCA, Discriminant Partial Least Squares), multivariate calibration (Partial Least Squares) and basic data preprocessing are considered. Methods for outlier detection and model validation are central parts of the course. Furthermore, a short introduction to modern spectroscopic methods will be given. Computer exercises and the project will be performed applying user-friendly software. A thorough introduction to the software will be given. | |||||||||||||||
| Teaching And Learning Methods | |||||||||||||||
| Lectures, guest lectures, cases, seminars and computer exercises will introduce the chemometric theory and the practical aspects of multivariate data analysis. In the project real data analytical problems are solved from a methodological perspective and the results are reported in written form. The project will be based on data sets from the Spectroscopic and Chemometrics group, Quality & Technology, Department of Food Science, KVL. | |||||||||||||||
| Course Litterature | |||||||||||||||
| Kim Esbensen: Multivariate Data Analysis - in Practice Notes, papers and other course material. | |||||||||||||||
| Course Coordinator | |||||||||||||||
| Lars Nørgaard, lan@life.ku.dk, Department of Food Science, Phone: 35333212 | |||||||||||||||
| Study Board | |||||||||||||||
| Study Committee LSN | |||||||||||||||
| Course Scope | |||||||||||||||
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