Abstract
The prediction of product acceptability is often an additive effect of individual fuzzy impressions developed by a consumer on certain underlying attributes characteristic of the product. In this paper, we present the development of a data-driven fuzzy-rule-based approach for predicting the overall sensory acceptability of food products, in this case composite cassava-wheat bread. The model was formulated using the Takagi-Sugeno and Kang (TSK) fuzzy modeling approach. Experiments with the model derived from sampled data were simulated on Windows 2000XP running on Intel 2Gh environment. The fuzzy membership function for the sensory scores is implemented in MATLAB 6.0 using the fuzzy logic toolkit, and weights of each linguistic attribute were obtained using a Correlation Coefficient formula. The results obtained are compared to those of human judgments. Overall assessments suggest that, if implemented, this approach will facilitate a better acceptability of cassava bread as well as nutritionally improved food.