2000 Fiscal Year Final Research Report Summary
Development of KANSEI Information Models to Evaluate the Preference for Fresh Fruits and Vegetables
| Project/Area Number |
10306016
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| Research Category |
Grant-in-Aid for Scientific Research (A).
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
生物環境
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| Research Institution | THE UNIVERSITY OF TOKYO |
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Principal Investigator |
SAGARA Yasuyuki Graduate School of Agricultural and Life Science THE UNIVERSITY OF TOKYO Associate Prof., 大学院・農学生命科学研究科, 助教授 (30012024)
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| Co-Investigator(Kenkyū-buntansha) |
NAGAI Hazime Research Institute on Product Development Researcher, 研究員
TONOIKE Misuo Life Electronics Research Center, Electrotechnical Laboratory, Chief Researcher, 電総研・大坂ライフエレクトロニクス研究センター, (研究職)室長
KAWAGOE Yoshinori Graduate School of Agricultural and Life Science THE UNIVERSITY OF TOKYO Assistant Prof., 大学院・農学生命科学研究科, 助手 (80234053)
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| Project Period (FY) |
1998 – 2000
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| Keywords | Fruits and Vegetables / Near Infrared Analysis / Sensory Evaluation / Texture / KANSEI Engineering / EEG / Ethylene / Rheology |
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| Research Abstract |
A new technique has been developed to measure the sugar content distribution for a cross section of a melon based on near infrared analysis. The wave length 902 and 874 nm were found to be useful for calibrating the sugar content and absorbency at each wave length. The cross sectional image of a melon was taken with a CCD camera combined with four band pass filters, and then the color map of sugar content was determined by applying the calibration curve obtained. The results indicated that the proposed method provide a tool to study and develop the new equipments for grading fresh fruits and vegetables depending on their internal chemical components or quality. The new indexes were proposed to evaluate the results of sensory test for the texture of apples based on the former mechanical and rheological measurements. The textural parameters for apple such as elasticity, hardness, crispness and chewiness were related with the indexes obtained from the curves of stress-strain as well as creep behaviors and also the coefficients of a 4-elements viscoelastic model. The results demonstrated that the new parameters were useful as information to input to a neural network model designed for predicting the results of sensory evaluation. The standard sensory evaluation method was presented and successfully applied to evaluate the performance of ethylene removing apparatus to maintain the freshness of tomato, which was stored in cold storage room for one week. The Electroencephalogram (EEG) against the stimulation of taste was measured to evaluate the variation of emotion, and the bitter and sweet tastes were found to provoke the feelings of stress and relaxation, respectively.
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