All who have preliminary knowledge about analytical chemistry, liquid chromatograpy and mass spectrometry.
The students who have successfully passed the course will get certificate of University of Tartu.
Introductory level knowledge of analytical chemistry, as well as liquid chromatography and mass spectrometry is required. More advanced knowledge of analytical chemistry and introductory knowledge of mathematical statistics is an advantage.
This is a practice-oriented on-line course on validation of analytical methods, specifically using LC-MS as technique. The course introduces the main concepts and mathematical apparatus of validation, covers the most important method performance parameters and ways of estimating them.
The course contains lectures, practical exercises and numerous tests for self-testing. In spite of being introductory, the course intends to offer sufficient knowledge and skills for carrying out validation for most of the common LC-MS analyses in routine laboratory environment. The analysis methods for which there are examples are pesticide analyses in fruits and vegetables, perfluororalkyl acids in water, antibiotics in blood serum, glyphosate and AMPA in surface water, etc. It is important to stress, however, that for successful validation experience (both in analytical chemistry as such and also specifically in validation) is crucial and this can be acquired only through practice.
Accomplishing the graded tests which are provided in Moodle environment.
- the main performance parameters of analytical methods, what they show and which of them are particularly important in different situations;
- the main mathematical concepts and tools in method validation;
- the main approaches for evaluation of the performance parameters in the case of LC/MS analysis.
- decide what data are needed for evaluating the different method performance parameters, understand the meaning of the available data and decide whether the available data are sufficient;
- select the approach and design the experiments for obtaining suitable data;
- quantify the relevant performance parameters using the available data and assess whether the obtained values are realistic;
- assess the fitness of the method for the intended purpose based on the values of the evaluated performance parameters.