NFOK19003U Foodomics and Plant Foods

Volume 2024/2025

MSc Programme in Food Science and Technology


Foodomics & Plant Foods is a multidisciplinary course covering a broad domain of scientific methods applied to the analysis of small molecules in plant foods and other biological samples. The course introduces the basics of the foodomics, which is the research field investigating molecular composition of foods and their impact on human health and wellbeing. Students will learn how molecular profiles of foods are screened using analytical platforms. The course will cover analytical platforms including Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry (LC-MS) and Proton (1H) Nuclear Magnetic Resonance (NMR) spectroscopy that are most often used to screen foods and human biofluids (blood, urine, and faecal). The course also covers design of foodomics/metabolomics experiments, from sample preparation to data acquisition and data pre-processing/analysis.

The course includes lectures, theoretical and hands-on laboratory exercises through which students will be familiarised with analytical platforms and standard operating procedures (SOPs) used for targeted and untargeted analysis of metabolites. The course also provides comprehensive teaching and practical exercises on foodomics/metabolomics data handling. This will include hands-on trainings in data pre-processing (metabolite identification and quantification) and data analysis using advanced data analysis methods.

Learning Outcome

The main aim of the course is to learn the state-of-the-art methods applied in high-throughput screening of small molecules in plant foods and other biological samples. Students will learn how to design, optimize, and evaluate foodomics/metabolomics protocols. Students will also learn data pre-processing methods to convert complex/raw data from analytical instruments into an annotated metabolite table. This will be achieved through hands-on training on foodomics/metabolomics data processing software. Finally, students will be familiarized with most common data analysis methods applied on foodomics/metabolomics datasets.


At the end of the course student will be able to do:



  • Describe principles of GC-MS, LC-MS and 1H NMR, and their applications in foodomics
  • Reflect on the advantages and disadvantages of GC-MS, LC-MS and 1H NMR
  • Identify suitable analytical platforms and methods for detection of one or more classes of substances
  • Describe foodomics data pre-processing and data analysis methods



  • Ability to identify critical points when designing and executing foodomics/metabolomics studies
  • Optimize biological sample processing (extraction) and analytical measurement steps
  • Ability to process complex foodomics/metabolomics datasets



  • Interpret, discuss and adapt foodomics/metabolomics methods from the literature
  • Process GC-MS, LC-MS, and 1H NMR data
  • Apply statistical analysis of foodomics/metabolomics datasets according to a pre-defined scientific question

See Absalon for a list of course literature

Basic knowledge in chemistry, analytical chemistry and multivariate data analysis (chemometrics) is recommended

Academic qualifications equivalent to a BSc degree is recommended.

Contact the course responsible if in doubt.
The course will combine lectures, theoretical and laboratory exercises. Lectures will be divided into clusters including analytical platforms, design of foodomics/metabolomics experiments, applications, metabolite identification, data pre-processing and data analysis. Each lecture cluster will be followed by a theoretic exercise where students will solve given tasks in small groups. Students will be familiarized with existing analytical platforms at during the laboratory exercises. During the laboratory exercises students will be divided into groups and apply analytical platforms to screen plant foods or other biological samples for small metabolites.
  • Category
  • Hours
  • Lectures
  • 56
  • Class Instruction
  • 28
  • Theory exercises
  • 32
  • Project work
  • 81
  • Guidance
  • 8
  • Exam
  • 1
  • Total
  • 206
7,5 ECTS
Type of assessment
Written assignment
On-site written exam, 1 hour under invigilation
Type of assessment details
The students will be evaluated based on a short written individual report (50%) and a following written exam (50%). Both the report and the written exam must be passed in order to pass the course.
Weight: Individual project report 50%, written exam 50%.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Same as ordinary exam

If the written individual report is not passed, a revised version must be submitted no later than three weeks before re-exam. 

Criteria for exam assesment

See Learning Outcome