Abstract
Simulation models for Baumé and alcohol concentration from the 11th day to the end of the sake mashing were constructed using a fuzzy neural network (FNN). The models could simulate the time courses of Baumé and alcohol concentration in 17 actual sake mashings. Average errors at the ends of the mashings were 0.22 and 0.40% for Baumé and alcohol concentration, respectively. By applying a genetic algorithm (GA) with the simulation models, temperature time courses were calculated with good accuracy, and the target values for Baumé and alcohol concentration on the final day could be achieved. To make a variety of sakes with different qualities, temperature courses were calculated against 3 target values: higher (+0.3), ordinary (0.0), and lower (-0.3) final day Baumés. The calculated temperature courses were found to be similar to a Toji's (expert's) strategy for making decisions on temperature. By applying this procedure, quality control of sake can be realized.
| Original language | English |
|---|---|
| Pages (from-to) | 331-337 |
| Number of pages | 7 |
| Journal | Seibutsu-kogaku Kaishi |
| Volume | 76 |
| Issue number | 8 |
| Publication status | Published - Dec 1 1998 |
| Externally published | Yes |
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All Science Journal Classification (ASJC) codes
- Biotechnology
- Food Science
- Applied Microbiology and Biotechnology
Cite this
Deciding the Temperature Course during Sake Mashing Using a GA-FNN for Quality Control of Sake. / Hanai, Taizo; Ueda, Naoyasu; Honda, Hiroyuki; Tohyama, Hisao; Kobayashi, Takeshi.
In: Seibutsu-kogaku Kaishi, Vol. 76, No. 8, 01.12.1998, p. 331-337.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Deciding the Temperature Course during Sake Mashing Using a GA-FNN for Quality Control of Sake
AU - Hanai, Taizo
AU - Ueda, Naoyasu
AU - Honda, Hiroyuki
AU - Tohyama, Hisao
AU - Kobayashi, Takeshi
PY - 1998/12/1
Y1 - 1998/12/1
N2 - Simulation models for Baumé and alcohol concentration from the 11th day to the end of the sake mashing were constructed using a fuzzy neural network (FNN). The models could simulate the time courses of Baumé and alcohol concentration in 17 actual sake mashings. Average errors at the ends of the mashings were 0.22 and 0.40% for Baumé and alcohol concentration, respectively. By applying a genetic algorithm (GA) with the simulation models, temperature time courses were calculated with good accuracy, and the target values for Baumé and alcohol concentration on the final day could be achieved. To make a variety of sakes with different qualities, temperature courses were calculated against 3 target values: higher (+0.3), ordinary (0.0), and lower (-0.3) final day Baumés. The calculated temperature courses were found to be similar to a Toji's (expert's) strategy for making decisions on temperature. By applying this procedure, quality control of sake can be realized.
AB - Simulation models for Baumé and alcohol concentration from the 11th day to the end of the sake mashing were constructed using a fuzzy neural network (FNN). The models could simulate the time courses of Baumé and alcohol concentration in 17 actual sake mashings. Average errors at the ends of the mashings were 0.22 and 0.40% for Baumé and alcohol concentration, respectively. By applying a genetic algorithm (GA) with the simulation models, temperature time courses were calculated with good accuracy, and the target values for Baumé and alcohol concentration on the final day could be achieved. To make a variety of sakes with different qualities, temperature courses were calculated against 3 target values: higher (+0.3), ordinary (0.0), and lower (-0.3) final day Baumés. The calculated temperature courses were found to be similar to a Toji's (expert's) strategy for making decisions on temperature. By applying this procedure, quality control of sake can be realized.
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M3 - Article
AN - SCOPUS:0345920166
VL - 76
SP - 331
EP - 337
JO - Seibutsu-kogaku Kaishi
JF - Seibutsu-kogaku Kaishi
SN - 0919-3758
IS - 8
ER -