Agricultural Information Research
Online ISSN : 1881-5219
Print ISSN : 0916-9482
ISSN-L : 0916-9482
Volume 21, Issue 1
Displaying 1-2 of 2 articles from this issue
Original Paper
  • Takeshi Osawa, Kazunori Kohyama, Tsuneo Kuwagata, Shigeto Sudo
    2012 Volume 21 Issue 1 Pages 1-10
    Published: 2012
    Released on J-STAGE: March 28, 2012
    JOURNAL FREE ACCESS
    Research and collaboration among environmental researchers requires the integration of various databases. Using "Web mash-up" technology, we developed a browser to integrate five independent agro-environmental databases and facilitate integrated use. Mirrors of the databases are integrated through a Web Application Program Interface (Web API) in a virtual integrated database. Users can download various data sets derived from the five databases in the database. Our model will enhance the integrated use of diverse databases and so contribute to several fields of study. We discuss the technical details of the system, the architectonics of the API, and the technical problems we encountered.
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  • Kaori Fujita, Junichi Sugiyama, Mizuki Tsuta, Toru Kozawa, Mario Shiba ...
    2012 Volume 21 Issue 1 Pages 11-19
    Published: 2012
    Released on J-STAGE: March 28, 2012
    JOURNAL FREE ACCESS
    We used fluorescence fingerprint (FF), also known as excitation-emission matrix spectroscopy, to develop a novel method to determine the mycotoxins nivalenol (NIV) and zearalenone (ZON) in wheat grain infected with Fusarium head blight. Grain samples were divided into four categories according to their level of blight damage. The amounts of NIV and ZON in milled samples were determined by liquid chromatography. NIV was not detected in all samples. ZON ranged from not detected to 0.71 ppm; its concentration was not correlated with damage, but it was predicted by FF values at various wavelengths by a partial least-squares regression model. The model showed a good fit with the calibration dataset (R2 = 0.993, SEP = 0.02 ppm) and the validation dataset (R2 = 0.974, SEP = 0.04 ppm). Plots of regression coefficients indicate that the prediction was based on peaks unique to each mycotoxin and some other weak fluorescence signals. These results confirm that FF can take rapid, non-destructive measurements and predict mycotoxins in wheat flour.
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