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Construction of Kansei engineering interface with respect to design and manufacturing of food suitable for consumer fevorableness

Research Project

Project/Area Number 11832013
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeSingle-year Grants
Section一般
Research InstitutionNagoya University

Principal Investigator

HONDA Hiroyuki  Graduate School of Engineering, Nagoya University, Associate Prof., 工学研究科, 助教授 (70209328)

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)
Project Period (FY) 1999 – 2000
Project Status Completed (Fiscal Year 2000)
Budget Amount *help
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 2000: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1999: ¥1,900,000 (Direct Cost: ¥1,900,000)
KeywordsKansei Engineering / Favorableness / Neural Networks / Modeling / Reverse calculation / Interface / Confidence / Genetic algorithm / 感性情報 / 品質設計 / 食嗜好 / 知識工学的手法 / 品質モデリング / 官能評価
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 … More 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

Report

(3 results)
  • 2000 Annual Research Report   Final Research Report Summary
  • 1999 Annual Research Report
  • Research Products

    (6 results)

All Other

All Publications (6 results)

  • [Publications] O.Tominaga: "Sensory Modeling of Coffee with a Fuzzy Neural Network"Food Science and Technology Research,. 7(3)(in press). (2001)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      2000 Final Research Report Summary
  • [Publications] Osamu Tominaga, Fumio Ito, Taizo Hanai, Hiroyuki Honda, and Takeshi Kobayashi: "Sensory modeling of coffee with a fuzzy neural network"Food Science and Technology Research. 7(3)(in press). (2001)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      2000 Final Research Report Summary
  • [Publications] O.Tominaga: "Sensory Modeling of Coffee with a Fuzzy Neural Network"Food Science and Technology Research,. 7(3)(in press). (2001)

    • Related Report
      2000 Annual Research Report
  • [Publications] 野口英樹 ほか: "FNNを用いたビール品質と醸造工程のモデル化"化学工学論文集. 25・5. 695-701 (1999)

    • Related Report
      1999 Annual Research Report
  • [Publications] Taizo Hanai ほか: "Application of artifical neural network and genetic algorithm for determination of process orbits in koji making process"Journal of Bioscience and Bioengineering. 87・4. 507-512 (1999)

    • Related Report
      1999 Annual Research Report
  • [Publications] 花井泰三 ほか: "知識情報処理の清酒醸造プロセスへの応用"化学工学論文集. 25・2. 163-168 (1999)

    • Related Report
      1999 Annual Research Report

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Published: 1999-04-01   Modified: 2016-04-21  

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