SubjectsSubjects(version: 948)
Course, academic year 2023/2024
  
Strategies of processing of chromatographic and mass spectrometric data - P323006
Title: Strategie zpracování chromatografických a spektrometrických dat
Guaranteed by: Department of Food Analysis and Nutrition (323)
Faculty: Faculty of Food and Biochemical Technology
Actual: from 2023 to 2023
Semester: winter
Points: winter s.:0
E-Credits: winter s.:0
Examination process: winter s.:
Hours per week, examination: winter s.:3/0, other [HT]
Capacity: unlimited / unknown (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Level:  
For type: doctoral
Note: course is intended for doctoral students only
can be fulfilled in the future
Guarantor: Stránská Milena doc. Ing. Ph.D.
Is interchangeable with: AP323006
Annotation -
Last update: Vlčková Martina Ing. (19.07.2018)
The aim of the course is to gain knowledge necessary for interpretation of data obtained by the analytical procedures, based on chromatographic and mass spectrometric principles, which are used for determination of different components of food. The following thematic areas are presented: (i) various chromatographic techniques hyphenated with different mass spectrometric approaches; (ii) interpretation of mass spectra; (iii) software for the identification of analytes and data evaluation; (iv) realization of non-target screening in food analysis; (v) quantification methods and techniques; confirmation; (vi) statistical methods for data processing (multivariate analysis).
Aim of the course -
Last update: Vlčková Martina Ing. (23.07.2018)

Students will understand the principles of modern separation and detection techniques based on high-resolution gas and liquid chromatography with different types of mass spectrometric detectors. They will get the knowledge of mass spectra interpretation, and learn about different softwares used for analytes identification and quantification. They will be able to implement target analyzes and non-target screening, taking into account available analytical instrumentation, and will have the knowledge necessary to properly evaluate and interpret the measured data.

Literature -
Last update: Stránská Milena doc. Ing. Ph.D. (15.11.2022)

Pradip K. Ghosh: Introduction to Protein Mass Spectrometry, 2015, ISBN: 978-0-12-802102-6, Elsevier

Mike S. Lee: Mass Spectrometry Handbook, 2012, ISBN:9780470536735, Wiley

Vitha, MF: Chromatography - Principles and Instrumentation, 2016, ISBN: 9781119270881, Wiley

Z: Kind T., Tsugawa H., Cajka T., Ma Y., Lai Z., Mehta S.S., Wohlgemuth G., Barupal D.K., Showalter M.R., Arita M., Fiehn O.: Identification of small molecules using accurate mass MS/MS search. Mass Spec Rev. 2017;9999:1–20.

Learning resources -
Last update: Vlčková Martina Ing. (19.07.2018)

https://massbank.eu/MassBank/

https://www.mzcloud.org/

https://chemdata.nist.gov/

www.bio-rad.com/en-cn/product/mass-spectral-databases

Teaching methods -
Last update: Vlčková Martina Ing. (19.07.2018)

Lectures

Requirements to the exam -
Last update: Vlčková Martina Ing. (18.07.2018)

None.

Syllabus -
Last update: Vlčková Martina Ing. (18.07.2018)

1. General requirements for the quality of data generated by chromatographic and mass spectrometric techniques; definition of basic concepts.

2. Gas chromatography coupled with mass spectrometry, ionization techniques in GC-MS, conventional detectors.

3. Liquid chromatography coupled with mass spectrometry, ionization techniques, conventional detectors.

4. Mass spectrometry (MS), types of mass spectrometers, acquisition modes.

5. Interpretation of GC-MS mass spectra.

6. Interpretation of LC-MS mass spectra.

7. Mass spectrometry data in food analysis.

8. Library spectra, retention indices.

9. Spectral deconvolution, software for identifying / interpreting analytes.

10. Application of non-target screening in food analysis.

11. Overview and application of different quantification techniques.

12. Criteria for confirmation in mass spectrometry.

13. Significant statistical methods for data processing.

14. Software for automatic data processing.

Entry requirements -
Last update: Vlčková Martina Ing. (18.07.2018)

None.

Registration requirements -
Last update: Vlčková Martina Ing. (18.07.2018)

None

Course completion requirements -
Last update: Vlčková Martina Ing. (23.07.2018)

oral exam, passing the test

 
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