Quantification of favorableness to foods by information processing on human sense
| Project/Area Number |
09838017
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
感性工学
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| Research Institution | Nagoya University |
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Principal Investigator |
HONDA Hiroyuki Graduate School of Engineering, Nagoya University Associate Professor, 工学研究科, 助教授 (70209328)
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| Co-Investigator(Kenkyū-buntansha) |
HANAI Taizo Graduate School of Engineering, Assistant Professor, 工学研究科, 助手 (60283397)
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| Project Period (FY) |
1997 – 1998
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| Project Status |
Completed (Fiscal Year 1998)
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| Budget Amount *help |
¥3,200,000 (Direct Cost: ¥3,200,000)
Fiscal Year 1998: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1997: ¥2,600,000 (Direct Cost: ¥2,600,000)
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| Keywords | Kansei Engineering / Food / Neural Networks / Modeling / Quantification / The Sense of Taste / Sensing / Favorableness |
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| Research Abstract |
Quantification of favorableness to foods such as beer, Ginjo sake and coffee, was carried out and the following results were obtained.
(1) Quality modeling of coffee was constructed. The analytical data from 67 samples obtained by dripping and those sensory evaluation data were collected. When fuzzy neural network (FNN) was applied to the modeling, the FNN model acquired showed the higher accuracy on estimation of sensory evaluation, compared with the model by the conventional method, multi-regression analysis.
(2) FNN and HFNN (hierarchical fuzzy neural network) were applied in order to construct the models estimated from the analysis data for the sensory evaluations of various Ginjo sake samples. Errors estimated by FNN and HFNN models were about 10% and 7%, respectively. Selected input variables using FNN and HFNN were in good agreement with expert's experiences. By the analysis of fuzzy rules, qualitative effects of these input variables were almost the same as expert's experiences.
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3) Models for sensory evaluation of beer and beer brewing process were constructed using FNN.A new method for optimal model selection using genetic algorithm and SWEEP operator method was compared with a conventional method using parameter increasing method. As the result, the new method was useful for the optimal model selection by simplifying the model structure, improving the reliability of fuzzy rules and fastening the calculation speed (about 10 times as fast as conventional method) for constructing the model with high accuracy. The important variables were selected as the input variables and the obtained fuzzy rules in modeling coincided well with knowledge data bases acquired by process operators.
(4) Fundamental research on tasting sensor was carried out. The gene encoding a gustatory receptor was cloned and the purification method of the receptor protein from recombinant Escherichia coli was established. When the purified protein was dipped on the sensor tip (i.d. 3mm) of surface plasmon resonance analyzer, the change of analyzer output could be detected. Therefore, the tasting sensor associated with a specific sensing molecule, gustatory receptor, is possible to be developed. Less
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Report
(3 results)
Research Products
(7 results)
