LLEK10246U  Advanced Chemometrics

Volume 2017/2018
Education

MSc Programme in Biology-Biotechnology
MSc Programme in Agriculture
MSc Programme in Food Science and Technology
MSc Programme in Environmental Chemistry and Health/Environmental Science

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:

Knowledge

  • 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


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 advantages and drawbacks of advanced methods

See Absalon for specific course literature

Competences in the field of Exploratory Data Analysis / Chemometrics (experience with PCA and PLS regression).
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.
Written
Oral
Individual
Collective
Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
Credit
7,5 ECTS
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%
Exam registration requirements

All reports must be approved before the exam.

Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Possibility to re-submit project reports two weeks before the re-examination, otherwise same as ordinary exam.

Criteria for exam assesment

See Learning Outcome

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