| 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 group report in due time before the oral examination. At the individual oral examination the students will be examined in the report as well as the examination requirements. Weight: Oral examination in project report and in the examination requirements 100% 7-point scale, internal examiner | ||||||||||||||
| Organisation of Teaching | Lectures (33%), exercises (33%) and project work (34%) | ||||||||||||||
| Block Placement | Block 1 Week Structure: B | ||||||||||||||
| Teaching Language | English | ||||||||||||||
| Restrictions | 50 | ||||||||||||||
| 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, Partial Least Squares Discriminant Analysis), 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. | |||||||||||||||
| Learning Outcome | |||||||||||||||
| The course introduces basic chemometric methods (PCA, PLS, PLS-DA 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. After completing the course the student should be able to: Knowledge: Describe chemometric methods for multivariate data analysis (exploration, classification and regression) Describe techniques for data pre-preprocessing Describe techniques for outlier detection Describe method validation principles Describe methods for variable selection Skills: Apply theory on real life data analytical cases Apply commercial software for data analysis Report in writing a full data analysis of a given problem including all aspects presented under Knowledge. Competences: Discuss and respond to univariate versus multivariate data analytical methodology in problem solving in society | |||||||||||||||
| Course Litterature | |||||||||||||||
| Textbook: See the web-site. Notes, papers and other course material. | |||||||||||||||
| Course Coordinator | |||||||||||||||
| Rasmus Bro, rb@life.ku.dk, Department of Food Science/Quality and Technology, Phone: 35333296 | |||||||||||||||
| Study Board | |||||||||||||||
| Study Committee LSN | |||||||||||||||
| Course Scope | |||||||||||||||
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