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270275 VO Chemometrics and Data Analysis in Multidimensional Analysis (2020W)

1.50 ECTS (1.00 SWS), SPL 27 - Chemie

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

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

Modern analytical methods for continuously monitoring e.g. industrial processes or environments usually need to simultaneously acquire a range of analytes. This leads to large amount of data in comparably short time. Their evaluation thus usually needs computer assisted methods as they are developed by chemometrics, whose main fields are:
- 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.

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

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: Mo 30.08.2021 15:48