Multivariate Datanalysis and Chemometrics
Academic Study Board of the Faculty of Engineering
Teaching language: English
EKA: T210032102
Censorship: Second examiner: None
Grading: Pass/Fail
Offered in: Odense
Offered in: Autumn
Level: Master
Course ID: T210032101
ECTS value: 5
Date of Approval: 31-08-2018
Duration: 1 semester
Version: Archive
Course ID
Course Title
ECTS value
5
Internal Course Code
Responsible study board
Date of Approval
Course Responsible
Programme Secretary
Offered in
Level
Offered in
Duration
Mandatory prerequisites
Knowledge of elementary statistics is required, e.g. from 4. semester on BSc in Engineering (Chemistry and Biotechnology).
Learning objectives - Knowledge
- Explain the statistical methods simple and (multivariate) multiple linear regression, principal component analysis, principal component regression and partial least squares, both in scalar and matrix/vector notation.
- Explain the main challenges and to identify the issues that can appear in chemometric calibration exercises.
- Explain the selection of the number of scores.
Learning objectives - Skills
- Apply the most important chemometric methods to solve selected multivariate calibration problems.
- Apply a statistical software package, such as R, for solving concrete multivariate calibration problems.
- Describe and conclude from result of a chemometric analysis and in the form of a report.
Learning objectives - Competences
- Describe the advantages and disadvantages of different chemometric methods, in order to choose the correct method to solve a given multivariate calibration problem.
- 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.
Content
- 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.
URL for Skemaplan
Teaching Method
Number of lessons
48 hours per semester
Teaching language
Examination regulations
Exam regulations
Name
Exam regulations
Tests
Exam
EKA
T210032102
Name
Exam
Description
Three projects with 3 project reports with analyses of chemometric data sets must be handed in on time and in accordance with the requirements specified at the start of the semester.
Assessment by the teacher of the three reports.
Form of examination
Internship, written report
Censorship
Second examiner: None
Grading
Pass/Fail
Identification
Student Identification Card - Exam number
Language
English
ECTS value
5