270275 VO Chemometrics and Data Analysis in Multidimensional Analysis (2020W)
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Details
max. 6 participants
Language: German
Examination dates
Friday
15.01.2021
Friday
05.02.2021
Monday
08.02.2021
Friday
12.02.2021
Tuesday
02.03.2021
Wednesday
03.03.2021
Wednesday
24.03.2021
Thursday
22.04.2021
Thursday
17.06.2021
Wednesday
21.07.2021
Monday
29.11.2021
Thursday
02.12.2021
Tuesday
07.12.2021
Lecturers
Classes
Vorlesung wird ab Mitte November aufgezeichnet und online auf Moodle gestellt und nach Vereinbarung mit BBB-Sessions kombiniert.
Information
Aims, contents and method of the course
Assessment and permitted materials
Oral exam based on individual appointment. Passing threshold at 50%, above that linear scale, i.e 50-62% "genügend", 62,5-75% "befriedigend", 75,5-88% "gut", darüber "sehr gut".
Minimum requirements and assessment criteria
Students will be familiar with the theoretical background of modern data analysis strategies by the end of the lecture. After passing the exam, they will therefore be able to introduce themselves rapidly into solving concrete problems with standard software (such as e. g. MatLab).
Examination topics
according to lecture material: digital filters, correlation analysis, principal component analysis, cluster analysis, experimental design, multivariate regression
Reading list
pdf der Verwendeten PowerPoint-Präsentation
- Matthias Otto, Chemometrie, ISBN 978-3527288496
- Richard G. Brereton, Chemometrics: Data Analysis for the Laboratory and Chemical Plant, ISBN: 978-0-471-48978-8
- Matthias Otto, Chemometrie, ISBN 978-3527288496
- Richard G. Brereton, Chemometrics: Data Analysis for the Laboratory and Chemical Plant, ISBN: 978-0-471-48978-8
Association in the course directory
AN-1, AN-4, BC-1, CHE II-1, 2 LA-Ch 32-34, LMC-C1, LMC C1
Last modified: Sa 08.07.2023 00:22
- Patter Recognition: (hierarchical) cluster analysis, principal component analysis
- Modelling of Data: regression methods in one and more variables, principal component analysis, neural networks.
- Time Series Analysis: autocorrelation functions
- Quality Assurance and Good Laboratory Practice
- Experimental Design.
In addition to simultaneously analyzing multicomponent mixtures, chemometrics also allows to classify samples according to not directly quantifiable criteria, such as discriminating different wines from each other by their smell or taste with so-called "artificial noses" or "artificial tongues". The main focus of the lecture will be on the actual analytical application of these techniques. Therefore, in-deep mathematical derivations will be foregone as far as possible.