Fuzzy Models to Deal with Sensory Data in Food Industry

摘要

Sensory data are, due to the lack of an absolute reference, imprecise and uncertain data. Fuzzy logic can handle uncertainty and can be used in approximate reasoning. Automatic learning procedures allow to generate fuzzy reasoning rules from data including numerical and symbolic or sensory variables. We briefly present an induction method that was developed to extract qualitative knowledge from data samples. The induction process is run under interpretability constraints to ensure the fuzzy rules have a meaning for the human expert. We then study two applied problems in the food industry: sensory evaluation and process modeling.