Analytical Chemistry, Chemometrics
15 credits cr.
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The training is intended to deepen participants' knowledge of experimental design, optimization and regression methods in the following principal fields: analytical-chemical process control, spectroscopic methods, separation methods.
The training is intended to deepen participants' knowledge of experimental design, optimization and regression methods in the following principal fields: analytical-chemical process control, spectroscopic methods, separation methods as well as basic research in analytical chemistry and other laboratory research. The course provides practical and theoretical skills in multivariate data analysis, regression models, data compression and information extraction applied to information-chemical data.
The course consists of lectures, seminars, and computer labs followed by independent work on applying the learned data analysis methods. The course has three parts, basic statistics, design of experiment and multivariate analysis.
In this part of the course you will learn how to compare the analysis results for two or more samples/batches/laboratories/methods, how to compare the variability of the results, how to know if your calibration graph actually is linear, and how to compare a more complex model to a simpler model. In the labs, you will use t-test, ANOVA, F-test, Lack-of-Fit, as well as many non-parametric alternatives to help you solve these challenges in data analysis.
Design of experiment
How to optimize a method so that you actually find the best conditions fastest? You will tackle this problem with Factorial Design, Mixed Design, Response Surface Design, and others. In the labs you will use these methods to optimize a GC method and see how to make your analytical methods green while not losingsensitivity!
In this part of the course we will focus on applying machine learning to the service of analytical data treatment. We will look at regression, classification, clustering, and dimensionality reduction. The question of interest is, for example, how can retention time in liquid chromatography be predicted for various chemical structures and then used to enable structural assignment?
Or how can the concentration of amine acids reveal the geographic origin of a bottle of white wine? Or how to avoid testing out all possible sample matrixes while developing an analysis method for pesticide analysis in fruits and vegetables?
We will end this part of the course with an in-house Gaggle challenge!
Theory 7.5 ECTS
Lab 7.5 ETCS
- Group exercises
- Written exam
- Lab reports
Matthew MacLeod, Matthew.MacLeod@aces.su.se
ScheduleThe schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
ContactChemistry Section & Student Affairs Office