Exploratory Data Analysis / Chemometrics

Course content

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 digitalization (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, environmental science, pharmaceutical science etc.

Methods for exploratory analysis (Principal Component Analysis), multivariate calibration (Partial Least Squares) and basic data preprocessing are considered. The mathematics behind most of the concepts will be given together with the practical applications and considerations of the methods.

Even more important, though, is the understanding and interpretation of the computed models. As is methods for outlier detection and model validation. Computer exercises and cases will be performed applying user-friendly software. A thorough introduction to the software will be given.


Bacheloruddannelsen i fødevarer og ernæring

Learning outcome

The course introduces basic chemometric methods (PCA and PLS) 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:


  • Describe chemometric methods for multivariate data analysis (exploration and regression)
  • Describe techniques for data pre-preprocessing
  • Describe techniques for outlier detection
  • Describe method validation principles
  • Understand the basics of the algorithms behind the PCA and PLS
  • Understand the math of data pre-processing.



  • Apply theory on real life data analytical cases
  • Apply commercial software for data analysis
  • Interpret multivariate models (both exploratory and regression).



  • Discuss and respond to univariate versus multivariate data analytical methodology in problem solving in society.

Lectures, cases and computer exercises will introduce the chemometric theory and the practical aspects of multivariate data analysis. During the course there will be a mix of short and long cases on real data analytical problems. The cases will mainly be based on data sets from research sections at the Department of Food Science. The final case is a written report the students will be evaluated on (see exam below).

See Absalon for a list of course literature

Competences in basic mathematics, statstics and data analysis are recommended

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Oral examination, 15 min
Written assignment, during course
Type of assessment details
Individual oral examination without preparation time in the course curriculum. The oral examination weights 50%, while the remaining 50% is based on the final case report (with clear indication of individual contributions). It is sufficient to pass the combined evaluation. However, omitting one of the two elements will be graded as a fail.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Same as ordinary exam.

Any previously passed part of the exam will count in the re-exam. A failed report has to be edited and re-submitted two weeks before the date of the re-examination.

Criteria for exam assessment

See Learning Outcome

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 24
  • Preparation
  • 59
  • Theory exercises
  • 35
  • Project work
  • 80
  • Guidance
  • 7
  • Exam
  • 1
  • English
  • 206


Course number
7,5 ECTS
Programme level

1 block

Block 3
The number of places might be reduced if you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study Board of Food, Human Nutrition and Sports
Contracting department
  • Department of Food Science
Contracting faculty
  • Faculty of Science
Course Coordinators
  • Åsmund Rinnan   (3-656576446a737368326f7932686f)
  • Rasmus Bro   (2-75654369727267316e7831676e)

Internal teachers from FOOD

Saved on the 19-02-2024

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students