ファジィニューラルネットワーク推論モデルを用いた吟醸もろみの発酵試験

To examine applicability of a fuzzy neural network (FNN) to decision of <I>moromi</I> temperature in <I>ginjo</I>-sake brewing process, two kinds of <I>ginjo</I>-sake were made and compared. Temperature of the one moromi was calculated automatically by the FNN, while that of the other was manually managed by the <I>toji</I>, a sake-brew master. Baumé alcohol concentration and temperature from 25 kinds of <I>ginjo moromi</I> made in 17 sake breweries in Aichi prefecture from 1989 to 1991 were used to construct the FNN model for decision of <I>ginjo moromi</I> temperature.<BR>Each sake brewing employed 100 kg total Wakamizu rice polished to 50% and <I>sokujomoto</I> made by using a <I>ginjo</I>-sake yeast, <I>S. cerevisiae</I> FIA-2 strain. Temperatures during the first 11 days changedsimilarly for the both <I>moromi</I>. However, temperatures from 12 to 29 days in the case of the FNN control were 0.5-1.5°C lower than those of the manual control. Althoughthere were some differences in the concentrations of flavor components (<I>e. g.</I>, 4.20 ppm iso-amylacetate in the sake by the FNN control, 3.82 ppm by the manual control), the seven panelists judged that <I>ginjo</I> flavor of the two kinds of sake was similar. The concentrations of chemical components, physicalproperties and sensory evaluation had almost the same values in these sake, suggesting that <I>ginjo</I>-sake can be made under the FNN control with almost the same quality as that made under the manual control of the <I>toji</I>.