NFOK19003U  Foodomics and Plant Foods

Volume 2019/2020
Education

MSc Programme in Food Science and Technology

Content

Today, vegetarian lifestyle and plant food based diet is a mega trend in the western world and it is expected to increase in coming years. Furthermore, limited natural resources and the continuously increasing world population demand increase in production of plant based foods in a sustainable way with a minimum environmental footprint. This requires optimization of plant food production in a new circular economy setup, “farm-to-shelf”, where evaluation of food composition, often referred as foodome, is an indivisible part of the process.

  • Foodome is defined as an entire collection of molecules in foods including small molecular components like vitamins, polyphenols, amino acids, fatty acids, and macromolecules such as proteins, carbohydrates and lipids as well as minerals
  • Foodomics is a newly emerged multidisciplinary field, screening the foodome and studying food and nutrition domains through the application of advanced omics-technologies (genomics, transcriptomics, proteomics and metabolomics)
  • Foodomics research plays a key role in optimization of food quality, sustainable plant food production, implementation of circular economy approaches (re-use of water and improved utilization of waste streams) in food production, personalised nutrition, and consumer’s wellbeing

 

Foodomics and 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 multidisciplinary foodomics approaches applied in studies focused on solving global challenges such as sustainable food production, improvement of food quality and nutritional value as well as to prevent food fraud and adulteration. The key component of the course is the advanced omics analytical platforms, including Nuclear Magnetic Resonance (NMR) Spectroscopy, and hyphenated chromatography-mass spectrometry, e.g. GC-MS, for chemical fingerprinting of plant foods and other biological samples including human blood, urine, muscle and faecal. The course provides in-depth knowledge and hands-on training on foodomics study design, acquisition and processing of high-throughput foodomics and metabolomics datasets, including identification of small molecules using spectral information. The course aims to close the current gap in applying foodomics methods to address real life problems related to sustainable food production, improvement of plant food products and human wellbeing.  

The course includes lectures, theoretical and practical exercises through which the students will be familiarised with the state-of-the-art hyphenated analytical platforms (e.g., GC-MS, NMR, NIR, and Raman) applied for small molecular screening, and to learn the principles behind these methods. In addition, method optimization and establishment of standard operating procedures (SOPs) will be taught using advanced Design of experiments (DoE) approaches. Moreover, the students will learn about different approaches used in small molecular screening including targeted metabolite analysis, profiling of one or few plant food metabolite classes as well as untargeted way of screening small molecules in foods, plants, and bio-fluid 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 algorithms.

Learning Outcome

 

The main aim of the course is to learn the state-of-the-art methods applied in screening of small molecules in plant foods, and other biological samples and gain competences in method development and optimization for targeted and untargeted analysis. 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 curtail steps of foodomics data generation including a design of experiment, optimization of protocols, data acquisition, data cleaning and data analysis.

 

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

 

Knowledge

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

Identify suitable analytical platforms and methods for detection of one or another class of substances

Reflect on the advantages and disadvantages of different analytical platforms

Describe foodomics data processing and analysis procedures

 

Skills

Design and execute foodomics studies

Develop and optimize analytical methods and sampling procedures

Ability to identify critical points in the workflow

Identify multivariate methods suitable to process complex datasets generated by different platforms

 

Competences

Perform independent plant food analysis on a desired analytical platform

Adjust and improve data acquisition methods

Interpret raw datasets and convert into informative metabolite table

Perform analysis of the generated foodomics datasets according to the investigated scientific question

 

 

See Absalon for a list of course literature

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

Academic qualifications equivalent to a BSc degree is recommended.
The course will combine lectures, seminars and hands on 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 plant food substances and processing/analysis of foodomics data. 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 last two weeks of the course students will carry out hands on data processing and analysis of foodomics datasets, either given by a tutor or on their own dataset.
Written
Oral
Collective
Credit
7,5 ECTS
Type of assessment
Oral examination, 30
Written assignment
Oral examination, 30 min

The students will be evaluated based on a written report (50%) 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
Exam period

30 min

Re-exam

Same as ordinary exam

Rejected written final report must be submitted no later than three weeks before re-exam

Criteria for exam assesment

See Learning Outcome

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