Sensory modeling of coffee with a fuzzy neural network

O. Tominaga, F. Ito, T. Hanai, H. Honda, T. Kobayashi

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)363-368
Number of pages6
JournalJournal of Food Science
Issue number1
Publication statusPublished - 2002
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Food Science


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