2000 Fiscal Year Final Research Report Summary
Construction of Kansei engineering interface with respect to design and manufacturing of food suitable for consumer fevorableness
| Project/Area Number |
11832013
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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 Institution | Nagoya University |
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Principal Investigator |
HONDA Hiroyuki Graduate School of Engineering, Nagoya University, Associate Prof., 工学研究科, 助教授 (70209328)
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| Co-Investigator(Kenkyū-buntansha) |
ITO Fumio Ajinomoto General Foods Co., Researchers, 研究所, 研究員
HANAI Taizo Graduate School of Engineering, Nagoya University, Assistant Prof., 工学研究科, 助手 (60283397)
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| Project Period (FY) |
1999 – 2000
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| Keywords | Kansei Engineering / Favorableness / Neural Networks / Modeling / Reverse calculation / Interface / Confidence / Genetic algorithm |
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| Research Abstract |
In order to design and manufacture the food suitable for consumer fevorableness, construction of Kansei engineering interface was investigated.
(1) Quality models were constructed to predict sensory evaluation scores from the blending ratio of coffee beans. Twenty-two blended coffees were prepared from three representative beans and were evaluated with respect to ten sensory attributes by an expert panel 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. It thus constitutes a powerful tool for accelerating product development.
(2) In order to determine process variable, reverse calculation from fo
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od design was investigated. In such cases, genetic algorithm (GA) has been often used as a speedy and convenient searching method. However, if the learning data is relatively fewer compared with the width of the space involving the data, searched solution has never the confidence and it becomes completely different with correct solution. To overcome this problem, GA accompanied with estimation of confidence (CFGA) was proposed. More than 20 equations were selected as a candidate of confidential function (CF) in order to estimate the confidence of each solution. When the confidence was defined by both of errors of the nearest three data points and those Euclidian distances, correctness of searching results by the proposed GA became high.
In addition, active learning method using CF was proposed for FNN modeling. Using CF, we can know how much are there a crowd of data point in the located space. Therefore, the located space that the data points are needed can be actively suggested. In the calculation experiment, some mathematical equation was tested. as a model space. CF was found to be effective as a supporting method of active learning.
(3) CFGA was applied for determination of coffee blending ratio and determination of process variables of Koji mashing process. Blending ratio and process variables were estimated with high correctness by the use of CFGA.
(4) In order to develop a software of FNN modeling, of which the use can be easy for any person, FNN packaging was carried out. The prototype of software was achieved and it was including parameter increasing method for selection of input variables, FNN modeling as a core program for Kansei engineering, and GA for reverse calculation. Less
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Research Products
(2 results)
