*THIS COURSE HAS BEEN CANCELLED*

 

ECTS credits:
3

 

Course parameters:
Language: English
Level of course: PhD course
Time of year: October 2020
Hours:
3 days of lectures and exercises (24 h)
1 day of working with own data (8 h)
Preparation by reading selected book chapters and articles (20 h)
Writing report and prepare presentation (20 h)
Follow up 1 day presenting data analysis (8 h)
Course fee: 1750 DKK for PhD and master students and AU-FOOD staff (covers 1 year software license and coffee/bread/fruit in the morning and afternoon), 5000 DKK for others.
Capacity limits: max. 15, min 8 participants. PhD students have highest priority. In case physical presence is made impossible due to Covid-19, the teaching will be carried out as online teaching through Blackboard and Zoom.    

 

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 PhD 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
PhD student Katrine O. Poulsen, 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 homepage:
Blackboard

 

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:

Chemometrics:
5 October: Lectures
6 October: Lectures
8 October: Lectures
9 October: Workshop: Working with own data

16 October: Deadline for handing in report

21 October: Examination seminar (peer-feedback, teacher-feedback)

Metabolomics:

1 October: Lectures
2 October: Lectures
9 October: Data preparation, workshop

16 October: Deadline for handing in report

23 October: Notice of assessment

 

Place:
Department of Food Science
Agro Food Park 48, 8200 Aarhus N.
Building 5910, room 214  

 

Registration:
Deadline for registration is 1 September 2020. Information regarding admission will be sent out no later than two workdays after registration deadline.

For registration: Opens later

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

 

Course fee:
Metabolomics only (covering bread/coffee/fruit and folder):
PhD students and master students enrolled at Danish Universities and AU staff: 500 DKK
Others: 2500 DKK

Chemometrics only (covering bread/coffee/fruit, folder and LatentiX license):
PhD students and master students enrolled at Danish Universities and AU staff: 1750 DKK
Others: 5000 DKK

Metabolomics and Chemometrics (covering bread/coffee/fruit, folder and LatentiX license):
PhD students and master students enrolled at Danish Universities and AU staff: 2250 DKK
Others: 7500 DKK