NFOK19003U Foodomics and Plant Foods

Volume 2025/2026
Education

MSc Programme in Food Science and Technology

Content

Foodomics & Plant Foods is a multidisciplinary course that encompasses a wide range of scientific methods applied to the analysis of small molecules in plant foods and other biological samples. The course introduces the fundamentals of foodomics, a research field focused on investigating the molecular composition of foods and their impact on human health and well-being. Students will learn how to conduct small molecule analysis using various analytical platforms. The course covers key techniques, including Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry (LC-MS), and proton (1H) Nuclear Magnetic Resonance (NMR) spectroscopy—methods frequently employed for molecular screening of complex samples such as foods and human biofluids (blood, urine, and feces).

The course provides instruction on experimental design, covering the entire process from sample preparation to data acquisition, pre-processing, and data analytical methods applied on foodomics and metabolomics datasets. The course features lectures, theoretical discussions, and laboratory exercises to familiarize students with the use of analytical platforms and standard operating procedures (SOPs) for targeted and untargeted metabolite analysis.

Learning Outcome

The primary goal of this course is to equip students with state-of-the-art methods used in the high-throughput screening of small molecules in plant foods and other biological samples. Students will learn to design, optimize, and evaluate foodomics/metabolomics protocols, as well as apply data pre-processing techniques to convert complex raw data from analytical instruments into annotated metabolite tables. This will be accomplished through hands-on training using specialized foodomics/metabolomics data processing software for identification and quantification of metabolites. Additionally, students will be introduced to common data analysis methods applied in modern data science.

By the end of the course, students will be able to:

Knowledge:

  • Describe the principles of GC-MS, LC-MS, and 1H NMR, and their applications in foodomics.
  • Reflect on the advantages and limitations of GC-MS, LC-MS, and 1H NMR techniques.
  • Identify appropriate analytical platforms and methods for detecting specific classes of substances.
  • Explain foodomics data pre-processing and data analysis methods.

 

Skills:

  • Identify critical points in the design and execution of foodomics/metabolomics studies.
  • Optimize biological sample processing (extraction) and analytical measurement steps.
  • Process complex foodomics/metabolomics datasets effectively.


Competencies:

  • Interpret, discuss, and adapt foodomics/metabolomics methods from the scientific literature.
  • Process GC-MS, LC-MS, and 1H NMR data.
  • Apply statistical analysis to foodomics/metabolomics datasets in response to predefined scientific questions.
Literature

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 www.food.ku.foodomics 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
Written
Collective
Credit
7,5 ECTS
Type of assessment
On-site written exam, 1 hour under invigilation
Written assignment
Type of assessment details
The students will be evaluated based on a short written individual report (50%) and a following On-site written exam (50%) with invigilation. Both the report and the written exam must be passed in order to pass the course.
Weight: Individual project report 50%, On-site written exam 50%.
Aid
Only certain aids allowed (see description below)

On-site written exam (1 hour): Only written aids allowed.

Written individual report. All aids are allowed.

Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

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