A Bayesian network is a probabilistic model that represents graphically the conditional dependencies between variables. Researchers often use consumer and expert panels to collect various quantitative measures of a product’s appeal. These measures may include contextual, sensory, emotional, usage and analytical product scores as well as measures of consumer acceptability. A Bayesian network of this data gives a graphical representation of both the key drivers of acceptability and also the inter-relationships and hierarchies between the descriptor variables, identifying both linear and non-linear relationships. We can use the resulting network for dynamic product optimisation or as a framework for further structured model building.
The example (graphic) uses data on consumer liking of different flavour fragrances together and shows the key linkages of liking to both emotional (pink) and sensory (mauve) descriptors and the interrelationships between the descriptors. The liking is influenced directly by descriptors such as “edible”, “Floral”, “Pleasant” and indirectly by descriptors such as “Sentimental” and “Overpowering”
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Bayesian networks in a sensory context
