Advanced Sensory Methods and Sensometrics

How does reformulating a product to reduce, e.g., its fat or sugar content, will affect consumer acceptance? Which sensory attributes explain best a consumer liking or healthiness perception? How can we approach this information? This advanced course provides students with the necessary tools to start becoming independent researchers in the growing area of sensory and consumer methods. Students will gain deeper insight in how to perform sensory tests (scope, application, testing environment, and methods) and conduct advanced analyses of sensory and consumer data. Theory will be combined with hands-on exercises including: analysis of sensory panel performance, descriptive sensory analysis (QDA, FCP, Flash Profile, CATA), holistic sensory methods (Sorting, Napping), acceptance testing and relations to external data (including Drivers of Liking, Preference Mapping, and Ideal Profile Mapping). The methods will be presented, discussed, and assessed for their merits and shortcomings in R&D.
The software used for the statistical analyses will be R. The students will become familiar on how to programme the basics in R language to be able to understand and analyse their data, and know the underlying principles of each statistical method in this course.

After successful completion of this course students are expected to be able to:

• Provide an overview of current and advanced analytical and affective sensory test methods, their scopes, and applications
• Demonstrate knowledge on experimental sensory test designs and data analysis by means of univariate and multivariate statistical methods
• Identify the applications and limitations of different data analyses
• Evaluate the sensory test objective in relation to selecting the appropriate test method and experimental setup
• Correctly interpret results of sensory experiments
• Reflect on sensory methods and data analysis tools in realistic sensory and consumer evaluation situations
• Demonstrate working knowledge of the statistical software program R in sensory applications

Assumed Knowledge:
HNH30506 Principles of Sensory Science; Advanced Statistics (MAT20306 Advanced Statistics or MAT24306 Advanced Statistics for Nutritionists).