2002 Fiscal Year Final Research Report Summary
Non-destructive Measurement of Pesticide Residues Based on Optical Measurement
| Project/Area Number |
11555110
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
Measurement engineering
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| Research Institution | Shinshu University |
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Principal Investigator |
TOBA Eiji Tex. Sci. Tech. Professor, 繊維学部, 教授 (60010956)
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| Co-Investigator(Kenkyū-buntansha) |
ISHIZAWA Hiroaki Tex. Sci. Tech. Associate Professor, 繊維学部, 助教授 (90345760)
NISHIMATSU Toyonori Tex. Sci. Tech. Professor, 繊維学部, 教授 (40252069)
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| Project Period (FY) |
1999 – 2002
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| Keywords | Diffuse reflectance infrared / Pesticide residues / Insecticides / Fungicides / Lettuces / Spectral image measurement / Principal component / PLS regression |
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| Research Abstract |
This project has proposed the non-destructive measurement system for the pesticide residues of the agricultural products that are eaten in raw. Diffuse reflectance infrared spectrum measurement was applied to the intact lettuces that contain pesticides through the cultivation. Partial least squares regression analysis was carried out by using the spectra and the residual values of pesticides obtained by chemical methods. The main results of this project are as follows.
(1) It takes two minutes to measure the pesticides in the sample that is shortest and easiest among the other methods.
(2) Using the real lettuce samples derived the optimum calibration models for methomyl insecticide, fenvalerate insecticide and benomyl fungicide by the partial least square regression.
(3) The cross validation of the partial least squares regression models of the first derivative spectra revealed that optimum number of factors for methomyl insecticide, fenvalerate insecticide and benomyl fungicide were 5, 6, and 4, respectively.
This project has also proposed the spectral image measurement for the food inspection based on the diffuse reflection of infrared light from lettuces or apples. The spectral image could provide the information not only on the chemical content in sample, but also on the human taste preferences such as freshness or juiciness. This invisible spectral image technology could be useful for the field information server of the food safety and/or quality.
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