Sensory evaluation and food preferences - Classic and digital approaches - 5 ECTS - 2025
PhD School at the Faculty of SCIENCE at University of Copenhagen
Aim and content
The PhD course is a biennial intensive course for PhD students in need of carrying out sensory or consumer acceptance studies as part of their PhD project. The course introduces the foundations of sensory science and practice of performing sensory tests with trained panels and by consumers including their design and execution and assessing sensory and consumer data.
In the 2025 edition of the course, we include the new possibilities of machine learning methods like those based on Natural Language Programming that can be used to enrich sensory and consumer research. Students will have hands-on practice with R programming. Furthermore, we will introduce how AI tools can be used in processing of sensory and consumer data.
The course consists of intensive course days in Copenhagen in addition to a course report on a sensory or consumer food choice behaviour topic related to PhD studies, including a perspective section on the role of machine learning on the chosen topic.
Course content:
Day 1 (half day): Course introduction, the senses and principles of sensory testing
Day 2: Sensory testing in practice: Descriptive analysis and rapid methods with exercises
Day 3: Consumer preferences and beyond: theory and practical exercises
Day 4: Introduction to machine learning and applications of machine learning in sensory research
Day 5: Machine learning in consumer research, course wrap-up (half day)
Formal requirements
Requirements:
- PhD students in need of carrying out sensory or consumer acceptance studies as part of their PhD project. Other researchers from academia/industry also accepted if space allows.
- Short motivation letter (1-2 paragraphs) to explain why they are taking the course and what they expect to get out of the course
• Knowledge of basic statistics
• Basic knowledge of R (otherwise we will give suggested resources to review beforehand)
Learning outcome
Knowledge:
• Understand principles of sensory measurements and evaluation methods
• Understand healthy food preferences in different situational contexts
• Understand common machine learning concepts, including associated algorithms and programmatic tools
Skills:
• Be able to design sensory experiments, analyse and interpret sensory results
• Be able to explore, regress, and classify quantitative and natural language data
Competences:
• Can critically assess literature in the fields of sensory and consumer research.
• Can select appropriate machine learning methodologies for solving research problems in sensory and consumer science.
• Can carry out appropriate sensory and consumer methodologies in own research project.
Literature
Reading materials will be selected for the course by the organisers and the guest lecturers. They will be distributed to the students approximately two weeks before the course.
Target group
PhD students working with sensory or consumer data from all areas, including but not limited to food science, nutrition, biology, agricultural sciences, psychology, neuroscience, marketing
Teaching and learning methods
The course consists of one or two modules:
I. Intensive course program in Copenhagen, Denmark including networking
II. Individual assignment and preparation of a report (deadline 15 November 2025)
Lecturers
Guest lecturers:
TBD
Remarks
The course fee includes course materials, coffee/tea during breaks, lunches and networking dinner. Participants should cover their own expenses of travel and accommodation.
The course fee will depend on the affiliation of the participants as follows:
• PhD students: 2,000 DKK
• Postdoctoral university staff/Non-profit: 4,000 DKK
• Industry/For-profit: 9,000 DKK
Full refund if cancellation latest two weeks before course start.
Co-organisers are Professors Wender Bredie and Karin Wendin