Foodomics and Plant Foods

Course content

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 also learn how molecular fingerprints of foods can be related to food production and detection of food fraud and adulteration. The course will cover advanced hyphenated analytical platforms, Gas Chromatography-Mass Spectrometry (GC-MS), Liquid Chromatography-Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance (NMR) Spectroscopy that are most often used in chemical fingerprinting of plant foods and other biological samples including human blood, urine, muscle and faecal. The course also provides in-depth knowledge on large-scale foodomics/metabolomics study design, data acquisition, data pre-processing and data analysis, as well as identification of molecules using spectral information.

The course includes lectures, theoretical and practical exercises through which the students will be familiarised with analytical platforms, method optimization and establishment of standard operating procedures (SOPs) for targeted and untargeted analysis of metabolites in food and other biological samples. The course will also provide comprehensive teaching and practical exercises on data handling prior to convert raw instrumental data into an informative metabolite table. This will include hands-on trainings in processing and analysis of large foodomics datasets using advanced multivariate data analysis methods. Thus, if possible, students can bring their own foodomics/metabolomics datasets to train on them during the course.

Education

MSc Programme in Food Innovation and Health

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 performed within foodomics studies. Students will also learn data handling approaches to translate complex foodomics datasets into chemical information and to interpret the results. This will be achieved by learning crucial steps in foodomics, including optimization of protocols, data acquisition, data processing and data analysis.

 

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

 

Knowledge

Potentials of high-throughput and untargeted screening of small molecules

Explain and understand existing methodologies used in foodomics studies for small molecular analysis

Identify suitable analytical platforms and methods for detection of one or more classes of substances

Reflect on the advantages and disadvantages of different analytical platforms

Describe foodomics data processing and analysis procedures

 

Skills

Ability to identify critical points when designing and executing foodomics studies

Optimize biological sample processing and analytical measurement steps

Ability to process foodomics datasets

 

Competences

Interpret and be able to discuss and adapt foodomics/metabolomics methods from the literature

Process raw GC-MS, LC-MS, and NMR data and convert into an informative metabolite table

Perform chemometrics on foodomics data according to the investigated scientific question

 

The course will combine lectures, theoretical and practical exercises on data processing and analysis of foodomics datasets. Lectures will be divided in four clusters; analytical platforms in foodomics, design of experiment in small molecular analysis, assignment of metabolites and foodomics data pre-processing and data analysis. Each lecture cluster will be followed by a theoretic exercise where students will be divided in small groups to solve given tasks. Students will be familiarised with existing analytical platforms at KU.FOOD.FOODOMICS laboratory during the tours. During the period of two weeks, students will carry out hands on data processing and analysis exercise, either given by a tutor or working with their own dataset.

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.

Written
Oral
Collective
ECTS
7,5 ECTS
Type of assessment
Oral examination, 20
Written assignment
The students will be evaluated based on a written report (50%) in groups of 3-4 students and a following final individual oral examination based on a presentation and discussion of the report and the course curriculum (50%). Both the report and the oral examination must be passed in order to pass the course.
Weight: Project report 50%, Oral examination 50%.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Criteria for exam assessment

See Learning Outcome

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 56
  • Class Instruction
  • 28
  • Theory exercises
  • 32
  • Project work
  • 81
  • Guidance
  • 8
  • Exam
  • 1
  • English
  • 206