| Project/Area Number |
16560374
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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 |
Measurement engineering
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| Research Institution | Kinki University |
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Principal Investigator |
EZAKI Shu Kinki University, School of Human-Oriented Science and Technology, Associate Professor, 産業理工学部, 助教授 (70185114)
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| Co-Investigator(Kenkyū-buntansha) |
IIYAMA Satoru Kinki University, Professor, 産業理工学部, 教授 (80176057)
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| Project Period (FY) |
2004 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2005: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 2004: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Keywords | chemical sense / taste sensor / lipid / polymer membrane / basic taste / mixed taste / mutual interaction |
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| Research Abstract |
The multichannel taste sensor with lipid/polymer membranes outputs a pattern peculiar to the characteristics of the membrane potentials. The pattern from the taste sensor is not expressing the quality of the taste directly. The purpose of this research is to change this output pattern into the intensity of the basic taste. The results are as follows.
1.The response characteristic of the taste sensor over mixed taste solution
The response characteristic to mixed taste solution was not fully investigated. In order to create a database of output patterns, the solution with various kinds and concentration of basic taste substances was measured. As a case study, the response characteristic to the salt solution by natural salt was also obtained.
2.Sensory tests by humans to mixed taste solution
Sensory tests to mixed taste solution were performed. It became clear that the results of the sensory test and the taste sensor had similar tendency on the interaction between taste substances.
3.Construction of the estimation system of the basic-taste intensity using database of the pattern to mixed taste solution
The conversion program from the output pattern to the taste intensity was constructed using a neural network. The good results were obtained on learning process.
4.Conversion from the output pattern of the taste sensor to the basic-taste intensity
After learning process, the unknown output patterns were inputted into the estimation system to estimate taste intensities. Good estimation of taste intensity was obtained in most of the output patterns.
5.Estimation of the bitter intensity of table salt
As an example of food, estimation of the bitter intensity contained in table salt was performed, and the good result was obtained.
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