SubjectsSubjects(version: 963)
Course, academic year 2013/2014
  
Interpretation of Chromatographic and Spectrometric Data - D323026
Title: Interpretace chromatografických a spektrometrických dat
Guaranteed by: Department of Food Analysis and Nutrition (323)
Faculty: Faculty of Food and Biochemical Technology
Actual: from 2013 to 2022
Semester: both
Points: 0
E-Credits: 0
Examination process:
Hours per week, examination: 0/0, other [HT]
Capacity: winter:unknown / unknown (unknown)
summer:unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Level:  
Additional information: http://web.vscht.cz/cajkat/ICSD.html
Note: course is intended for doctoral students only
can be fulfilled in the future
you can enroll for the course in winter and in summer semester
Guarantor: Lacina Ondřej Ing. Ph.D.
Stránská Milena prof. Ing. Ph.D.
Examination dates   Schedule   
Annotation -
This subject is focused on the interpretation of chromatographic and mass spectrometric data generated within analysis of various food components. The following topics are discussed: (i) connection of chromatographic and mass-spectrometric techniques; (ii) interpretation of mass spectra; (iii) software tools for analyte identification and data processing; (iv) non-targeted screening in food analysis; (v) techniques for the quantification of analytes and confirmation of analytes; (vi) statistical methods for the data processing and interpretation (multivariate analysis).
Last update: LACINAO (29.03.2014)
Aim of the course -

Students will be able to:

  • Apply the knowledge for using separation techniques (GC and LC) in combination with mass spectrometric detection for different analytical tasks; they will understand to the basic characteristics of different ionization techniques and mass spectrometers.
  • Analyse mass spectrum, find the molecular ion and analyze its isotopic envelope, calculate formula from simple spectrum and calculate number of double-bonds.
  • Qualitatively analyze LC-MS and GC-MS data; correctly apply smoothing algorithms; understand to spectral deconvolution; to use mass spectral libraries and retention indexes for the identification of analytes.
  • Quantify obtained data, confirm results, use different calibration approaches (external calibration curve, method of standard addition, internal standards, isotopic dilution) in LC-MS and GC-MS.
  • Apply basic chemometric tools for the analysis of data structure and get the maximum of information from recorded data sets.

Last update: LACINAO (29.03.2014)
Course completion requirements -

Written test and oral exam

Last update: LACINAO (30.03.2014)
Literature -

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.

Last update: Stránská Milena (15.11.2022)
Syllabus -

1. General requirements on quality of chromatographic and mass-spectrometric data, definition of basic concepts.

2. Connection of gas chromatography and mass spectrometry, ionization techniques, conventional detectors.

3. Connection of liquid chromatography and mass spectrometry, ionization techniques, conventional detectors.

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

5. Interpretation of GC-MS spectra.

6. Interpretation of LC-MS spectra.

7. Mass spectrometry in food analysis.

8. Spectral libraries, retention indexes.

9. Spectral deconvolution, Software tools for an analyte identification/interpretation.

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

11. Quantification techniques.

12. Confirmation criteria in mass spectrometry.

13. Statistical methods for data processing.

14. Software for automated data processing.

Last update: LACINAO (30.03.2014)
Entry requirements -

No requirements

Last update: LACINAO (30.03.2014)
Coursework assessment
Form Significance
Examination test 50
Oral examination 50

 
VŠCHT Praha