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Quantification of favorableness to foods by information processing on human sense

Research Project

Project/Area Number 09838017
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeSingle-year Grants
Section一般
Research Field 感性工学
Research InstitutionNagoya University

Principal Investigator

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

Co-Investigator(Kenkyū-buntansha) HANAI Taizo  Graduate School of Engineering, Assistant Professor, 工学研究科, 助手 (60283397)
Project Period (FY) 1997 – 1998
Project Status Completed (Fiscal Year 1998)
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)
KeywordsKansei Engineering / Food / Neural Networks / Modeling / Quantification / The Sense of Taste / Sensing / Favorableness
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.
( … More 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

Report

(3 results)
  • 1998 Annual Research Report   Final Research Report Summary
  • 1997 Annual Research Report
  • Research Products

    (7 results)

All Other

All Publications (7 results)

  • [Publications] T.Hanai: "Application of an artificial neural network and genetic algorithm for determination of process orbits in the koji making process" J.Biosci.Bioeng.87・4. 320-326 (1999)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1998 Final Research Report Summary
  • [Publications] Taizo Hanai, Eiji Ohkusu, Hiroyuki Honda, Fumio Ito, Motohiko Sugiura, Ichiro Asano and Takeshi Kobayashi: "Quality modeling for coffee using the knowledge information processing (in Japanese)" Nippon Shokuhin Kagaku Kogaku Kaishi. 44(8). 560-568 (1997)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1998 Final Research Report Summary
  • [Publications] Taizo Hanai, Hideki Noguchi, Hiroyuki Honda, Takeshi Furuhashi, Yoshiki Uchikawa, Masahiro Kamiya, Tohoru Ishii and Takeshi Kobayashi: "Quality model of Ginjo sake using hierarchical fuzzy neural network (in Japanese)" Nihon Fuzzy Gakkaishi. 10(2). 299-306 (1998)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1998 Final Research Report Summary
  • [Publications] Taizo-Hanai, Eiji Ohkusu, Toshihiko Ohki, Hiroyuki Honda, Hisao Tohyama, Takahiro Muramatsu and Takeshi Kobayashi: "Application of an artificial neural network and genetic algorithm for determication of process orbits in the koji making process" Journal of Bioscience and Bioengineering. 87(4). 320-326 (1999)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1998 Final Research Report Summary
  • [Publications] T.Hanai: "Application of an artificial neural network and genetic algorithm for determination of process orbits in the koji making process" J.Biosci.Bioeng.87・4. 320-326 (1999)

    • Related Report
      1998 Annual Research Report
  • [Publications] 花井泰三: "知識情報処理を用いたコーヒーの品質モデル" 日本食品科学工学会誌. 44(8). 560-568 (1997)

    • Related Report
      1997 Annual Research Report
  • [Publications] 花井泰三: "階層化ファジィニューラルネットワークを用いた吟醸酒の品質モデリング" 日本ファジィ学会誌. 10(2). 260-269 (1998)

    • Related Report
      1997 Annual Research Report

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Published: 1997-04-01   Modified: 2016-04-21  

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