Kursussøgning, efter- og videreuddannelse – Københavns Universitet

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Kursussøgning, efter- og videreuddannelse

Advanced Chemometrics

Practical information
Study year 2016/2017
Block 2
Programme level Full Degree Master
Course responsible
  • Rasmus Bro (2-81714f757e7e733d7a843d737a)
  • Department of Food Science
Course number: LLEK10246U

Course content

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.



Learning outcome

The course introduces advanced chemometric methods and their use on different kinds of multivariate data of relevance for research and development.

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

Recommended prerequisites

Competences corresponding to Exploratory Data Analysis / Chemometrics (must have experience with PCA and PLS regression).

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MSc Programme in Biology-Biotechnology
MSc Programme in Agriculture
MSc Programme in Food Science and Technology


Study Board of Food, Human Nutrition and Sports

Course type

Single subject courses (day)


1 block


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Teaching and learning methods

The students will be introduced to the theory through lectures and seminars. The students will work on data analytical problems using the taught methods and software to analyse data. The students can bring their own data analytical problems to work on; this requires that the course teachers consider the data as suitable to illustrate the taught methods. The results are presented in written reports which is orally defended at the end of the course.






See Absalon for specific course literature (see also course web-site).


Category Hours
Exam 1
Lectures 69
Preparation 110
Project work 26
English 206


Type of assessment

Oral examination, 20 min
The 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%


All aids allowed

Marking scale

7-point grading scale

Criteria for exam assessment

Cf. Learning Outcome

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.

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