2. Predictive Hedonics

Understanding, predicting, and modulating peripheral perception and coding mechanisms of chemosensory systems and developing the foundations for intelligent human-machine interfaces and chemosensors with human performance capabilities form the basis for predicting food quality in the future.

What determines how we evaluate food in terms of sensory properties? Is it possible to determine in advance how consumers will perceive the taste of a food based on its food constituents and the way it is produced? To answer these questions, Leibniz-LSB@TUM is researching the biomolecular mechanisms of human chemosensory perception, which are primarily influenced by the senses of smell and taste, as well as trigeminal stimuli and texture/mouthfeel. Through its research, Leibniz-LSB@TUM is helping to improve our understanding of how the sensory effector systems contained in food interact with peripheral chemoreceptors in the mouth and nose, and which coding mechanisms convert chemosensory stimuli into neural activity patterns. Based on these findings, Leibniz-LSB@TUM aims to use powerful analytical techniques to clarify how the hedonic evaluation of foods can be better predicted from the molecular signature of their food constituents (recording – assessment – prediction). The results should contribute to the targeted optimization of production and processing processes for the development of healthy and at the same time tasty foods. Last but not least, the research results should contribute to the development of intelligent human-machine interfaces for microsystem technology and high-performance sensors that can be used to give technical systems, such as robots, a sense of smell with human capabilities.

The research activities include in particular:

• Decoding and modulation of the translation of chemosensory food effector systems (active systems) into the regulatory mechanisms underlying human perception and contribution to the development of new approaches for behavioral change with regard to healthier diets.
• Generating understanding and predicting the significance of genetic polymorphisms and epigenetics for chemosensory perception and the development of food preferences.
• Automated monitoring of chemosensory effector systems and prediction of the evaluation of the aroma, taste, and texture of food.
• Creation of new system approaches for modulating sensory perception.
• Contribution to the development of chemosensor arrays with human performance and intelligent human-machine interfaces.