Sensory Modeling of Coffee with a Fuzzy Neural Network
- 1 January 2002
- journal article
- Published by Wiley in Journal of Food Science
- Vol. 67 (1) , 363-368
- https://doi.org/10.1111/j.1365-2621.2002.tb11411.x
Abstract
Models were constructed to predict sensory evaluation scores from the blending ratio of coffee beans. Twenty‐two blended coffees were prepared from 3 representative beans and were evaluated with respect to 10 sensory attributes by 5 coffee cup‐tasters and by models constructed using the response surface method (RSM), multiple regression analysis (MRA), and a fuzzy neural network (FNN). The RSM and MRA models showed good correlations for some sensory attributes, but lacked sufficient overall accuracy. The FNN model exhibited high correlations for all attributes, clearly demonstrated the relationships between blending ratio and flavor characteristics, and was accurate enough for practical use. FNN, thus, constitutes a powerful tool for accelerating product development.Keywords
This publication has 7 references indexed in Scilit:
- Application of an artificial neural network and genetic algorithm for determination of process orbits in the koji making processJournal of Bioscience and Bioengineering, 1999
- Optimizing Acceptability of Chicken Nuggets Containing Fermented Cowpea and Peanut FloursJournal of Food Science, 1997
- Surimi-Starch Interactions Based on Mixture Design and Regression ModelsJournal of Food Science, 1997
- Quality Modeling for Coffee Using the Knowledge Information Processing.Nippon Shokuhin Kagaku Kogaku Kaishi, 1997
- Predicting Milk Shelf‐life Based on Artificial Neural Networks and Headspace Gas Chromatographic DataJournal of Food Science, 1995
- Representation of Fuzzy Rules Using Neural NetworksJournal of Japan Society for Fuzzy Theory and Systems, 1993
- Experiments with MixturesJournal of the Royal Statistical Society Series B: Statistical Methodology, 1958