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Food Fingerprinting

Coordinator: Dr Biniam Kebede

Teaching Staff: Dr Biniam Kebede

Eligibility: Both Food Science (FOSC) and Consumer Food Science (CFSC)

Module description

Food fingerprinting is an approach that was adopted from the world of metabolomics to study food and nutrition domains at molecular level. By definition, Food fingerprinting is an ‘untargeted, multivariate approach’ in which as many compounds as possible of a particular food extract are detected. At the first instance, compounds are unknowns. Fingerprinting techniques (e.g. GC-MS, LC-MS) are applied to perform comparative analysis to find differences among samples. Using powerful data analysis methods (e.g. chemometrics), food fingerprinting results in the selection of (bio)markers: These markers are identified and can be linked to reaction pathways or particular food characteristics for more insight.

This course will provide a step-wise guide to apply food fingerprinting: starting from sample preparation to data analysis and finally interpretation.  Firstly, different sample extraction methods will be discussed; Secondly, advanced analytical equipment (such as GC-MS, LC-MS) to detect an increased number of compounds will be discussed; Finally, powerful chemometric data analysis techniques, such as multivariate data analysis, will be discussed.

The course will consist of lectures and practical data analysis session. Students will apply the multivariate data analysis on their own data.

Topics

  1. Different sample preparation techniques;
  2. Advanced instrumental techniques;
  3. Powerful data analysis methods;
  4. (bio)marker selection;
  5. Proper data interpretation.

Format

4 week intensive course, Semester TBC

The course consists of approximately 11 hours of lectures and 4 hours of data analysis sessions.  In the first two weeks, lectures on sample preparation, instrumental and data analysis methods will be provided. On the third week, practical data analysis session will be held, on a data station at the Food Science Department. Students will learn to perform multivariate data analysis on a data of their choice and will prepare a report. The last week will focus on data interpretation and revision.

Assessment

This module is worth 50% of a 20 credit paper, as follows;

  • Attendance / leading class discussion - 10%
  • Final written report - 20%
  • Oral Examination – 20%

Further information

Postgraduate courses available in the department