ST808: Multivariate Data Analysis and Chemometrics
calibration techniques and their applications in chemometrics.
The course builds on the knowledge acquired in the courses linear algebra and mathematical statistics.
- Give the competence
to plan and execute scientific projects at a high level, including the
management of work and development situations that are complex,
unpredictable and that require new problem solving skills
- Give skills to master computer calculations
- Give knowledge
about advanced models and methods in applied mathematics, based on
international research, and knowledge about application of these models
and methods on problems from various disciplines and from the private
Expected learning outcome
The learning objectives of the course are that the student demonstrates the ability to:
- describe the main chemometric methods for multivariate calibration and to know how to apply these in a specific context.
the application of a statistical software package, such as R, for
solving concrete multivariate calibration problems, and to be able to
describe the result of such an analysis in the form of a report.
the advantages and disadvantages of different chemometric methods, in
order to choose the correct method to solve a given multivariate
- describe the main methods for validation
and optimization of a given calibration method for a specific problem,
in order to assess the correctness of the method in the given context.
The following main topics are contained in the course:
- Repetition of basic concepts from statistics and matrix algebra.
- Introduction to chemometrics and multivariate calibration.
- Multiple linear regression analysis (MLR).
- The classical least squares method (CLS).
- Principal components analysis (PCA).
- Principal components regression (PCR).
- Partial least squares regression (PLS).
- Validation and optimization of calibration model.
Exam element a)
Evaluation is based on three project reports regarding chemometric data analyses set during the course
To be announced during the course
Reexam in the same exam period or immediately thereafter. The examination form for re-examination may be different from the exam form at the regular exam.
Indicative number of lessons
Activities during the study phase: To study the course material and familiarise oneself with the statistical analyses in the R software package, individually or through group work.
|08 - 09|
|09 - 10|
|10 - 11|
|11 - 12|
|12 - 13|
|13 - 14|
|14 - 15|
|15 - 16|