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.
| Original language | English |
|---|---|
| Pages (from-to) | 363-368 |
| Number of pages | 6 |
| Journal | Journal of Food Science |
| Volume | 67 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 1 2002 |
| Externally published | Yes |
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All Science Journal Classification (ASJC) codes
- Food Science
Cite this
Sensory modeling of coffee with a fuzzy neural network. / Tominaga, O.; Ito, F.; Hanai, Taizo; Honda, H.; Kobayashi, T.
In: Journal of Food Science, Vol. 67, No. 1, 01.01.2002, p. 363-368.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Sensory modeling of coffee with a fuzzy neural network
AU - Tominaga, O.
AU - Ito, F.
AU - Hanai, Taizo
AU - Honda, H.
AU - Kobayashi, T.
PY - 2002/1/1
Y1 - 2002/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0036191818&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036191818&partnerID=8YFLogxK
U2 - 10.1111/j.1365-2621.2002.tb11411.x
DO - 10.1111/j.1365-2621.2002.tb11411.x
M3 - Article
AN - SCOPUS:0036191818
VL - 67
SP - 363
EP - 368
JO - Journal of Food Science
JF - Journal of Food Science
SN - 0022-1147
IS - 1
ER -