LLEK10246U Advanced Chemometrics
MSc Programme in Biology -Biotechnology
MSc Programme in Agriculture
Basic chemometric methods like PCA and PLS are useful tools in
data analysis but in many data analytical problems more advanced
methods are necessary to solve the problems.
The methods studied in this course will be selected from these main topics: Data preprocessing methods, variable selection methods, clustering and classification techniques, calibration transfer methods, non-linear regression and multi-way methods.
Computer exercises on real data using commercial software are an integrated part of the course.
The course introduces advanced chemometric methods and their use
on different kinds of multivariate data of relevance for research
After completing the course the student should be able to:
- Summarize basic chemometric methods
- Describe advanced chemometric methods for multivariate (clustering, classification and regression) data analysis
- Describe advanced techniques for data pre-preprocessing
- Describe advanced methods for variable selection
- 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.
- Discuss advantages and drawbacks of advanced methods
Scientific papers, book chapters and course notes (see course web-site).
- Project work
- 7,5 ECTS
- Type of assessment
- Oral examination, 20 minThe students will hand in a number of written reports in due time before the oral examination. At the oral examination, the student will be examined in the reports as well as the curriculum.
Weight: Oral examination, 100%
- Exam registration requirements
- All reports must be approved before the exam
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
More than one internal examiner
- Possibility to re-submit project reports two weeks before the registration date of the re-examination, otherwise same as ordinary exam
Criteria for exam assesment
Cf. Learning outcome