Knowledge mining sensory evaluation data is a challenging process due to extreme sparsity of the data, and a large variation in responses from different members (called assessors) of the panel. The main goals of knowledge mining in sensory sciences are understanding the dependency of the perceived liking score on the concentration levels of flavors’ ingredients, identifying ingredients that drive liking, segmenting the panel into groups with similar liking preferences and optimizing flavors to maximize liking per group. Our approach employs (1) Genetic programming (symbolic regression) and ensemble methods to generate multiple diverse explanations of assessor liking preferences with confidence information; (2) statistical techniques to extrapolate using the produced ensembles to unobserved regions of the flavor space, and segment the assessors into groups which either have the same propensity to like flavors, or are driven by the same ingredients; and (3) two-objective swarm optimization to identify flavors which are well and consistently liked by a selected segment of assessors.
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The greater than normal number of samples were enabled by a proprietary method for delivering the flavor to the assessor which delays sensory fatigue.
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Veeramachaneni, K., Vladislavleva, E. & O’Reilly, UM. Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization. Genet Program Evolvable Mach 13, 103–133 (2012). https://doi.org/10.1007/s10710-011-9153-2
- Symbolic regression
- Sensory science
- Non-linear optimization
- Variable selection
- Pareto genetic programming
- Hedonic evaluation
- Complexity control