Sensory modeling of coffee with a fuzzy neural network

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

研究成果: Contribution to journalArticle査読

12 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)363-368
ページ数6
ジャーナルJournal of Food Science
67
1
DOI
出版ステータス出版済み - 2002
外部発表はい

All Science Journal Classification (ASJC) codes

  • 食品科学

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