ECTS credits: 3

Course parameters:

Language: English

Level of course: PhD course

Time of year: January and February 2026

Hours:

3 days of lectures and exercises (18 h)

1 day of working with own data (6 h)

Preparation by reading selected book chapters and articles (25 h)

Writing report, prepare presentation and feedback (25 h)

Follow up 1 day presenting data analysis (6 h)

 

Course fee: Free for PhDs (DK) and master students and staff at AU-FOOD, 5000 DKK for others.

 

Capacity limits: max 25, min 10

 

Objectives of the course:

The purpose of the course is to give an introduction to some of the common methods in multivariate data analysis, and give the students tools and knowledge to understand and perform PCA and PLS data analysis on their own data.

 

Learning outcomes and competences:

At the end of the course, the student should be able to:

• Arrange data in a matrix appropriate for PCA and PLS
• Apply PCA (exploration) and PLS (regression) on new data and analyze the results
• Compare and contrast the methods for a given data analysis situation considering the benefits and the pitfalls of the methods
• Apply the most common standardization methods appropriately
• Examine relevant plots for outliers in PCA and PLS and thereby classify severe outliers, consider borderline cases and argue for the classification
• Apply appropriate validation of PLS models and consider the number of PLS components
• Outline the most common preprocessing methods
• Outline classification methods such as SIMCA and discriminant PLS
• Interpret PCA and PLS models described in scientific literature and describe your own results in a scientific way
• Critically evaluate other students work based on model parameters and knowledge of model characteristics

Compulsory programme:

Attendance for a minimum of 80% of the theoretical and practical lessons is required to obtain the course diploma. Approved report.

 

Course contents:

Multivariate data analysis (chemometrics) can be used to solve problems involving large amounts of multivariate data generated by e.g. spectroscopy, chromatography or time series of many variables. In chemometrics informative patterns are found and interpreted instead of looking at classical, and often inadequate, univariate measures. Chemometrics is widely used in science and in scientific papers. It is important to know what features to use, how to use them correctly and how to interpret plots. Chemometrics include hypothesis generating methods, but can also be used for classification and prediction.

The course will give a thorough introduction to the chemometric methods, Principal Component Analysis (PCA) and Partial Least Squares (PLS) regression, including common data pre-processing.

Some mathematical and statistical expressions will be used in the course and a variety of data (e.g. chemical, sensory and spectroscopic data) will be used as examples.

 

Prerequisites:

Enrolled in a science based Ph.D. programme. Master students can participate as a part their master project in agreement with the supervisor.

 

Name of lecturers:

Assistant professor Ulrik Kræmer Sundekilde, Department of Food Science, Aarhus University, Denmark

 

Type of course/teaching methods:

Lectures, computer exercises, data analysis of your own data, writing and presenting report.

 

Literature:

Selected chapters and papers will be announced later.

 

Course assessment:

Passed/not passed assessment based on written report, presentation and discussion of results considering the learning outcomes.

 

Provider:

Department of Food Science

 

Special comments on this course:

The course is organized in combination with the PhD-course ‘Introduction to metabolomics’ and it is possible to follow both courses, although the courses can be taken individually if necessary.

 

Time:

Tuesday 20. January: Lectures and exercises

Wednesday 20. January: Lectures and exercises

Thursday 21. January: Lectures and exercises

Wednesday 28. January: Workshop: Working with own data

 

1. February: Deadline for handing in report

 

Tuesday 1. February: Examination seminar (peer-feedback, teacher-feedback)

 

Place:

Dept. Food Science

Agro Food Park 48, 8200 Aarhus N and

Agro Food Park 26, 8200 Aarhus N

 

Registration:

Deadline for registration is January 15, 2026. Information regarding admission will be sent out no later than two workdays after registration deadline.

 

For registration: e-mail: uksundekilde@food.au.dk

 

If you have any questions, please contact Ulrik Sundekilde, e-mail: uksundekilde@food.au.dk