Abstract
In order to eliminate the influence of scattering, the original spectrum was processed by multiplicative scatter correction (MSC). The effective band was selected according to the correlation coefficient method, and 8 characteristic wavelengths were selected by continuous projection algorithm combined with information entropy. Finally, the effective bands and different features were used to establish prediction model for mildew maize aflatoxin B1 and gibberellin content at wavelength by BP neural network. The results showed that the prediction model established by spectral information at 8 kinds of characteristic wavelengths was the best, with the correct prediction rate of aflatoxin B1 content of 98.74%, the root mean square error of 0.048 5, and the correct rate of gibberellin content prediction of 100%, and the square root error of 0.160 5. Therefore, the method of hyperspectral coupled with neural network is feasible to detect the aflatoxin B1 and gibberellin content in moldy maize.
Publication Date
11-28-2018
First Page
64
Last Page
69
DOI
10.13652/j.issn.1003-5788.2018.11.014
Recommended Citation
Guanghui, WANG and Yong, YIN
(2018)
"Detection of moldy maize aflatoxin B1 and gibberellinby hyperspectral coupled with neural network,"
Food and Machinery: Vol. 34:
Iss.
11, Article 13.
DOI: 10.13652/j.issn.1003-5788.2018.11.014
Available at:
https://www.ifoodmm.cn/journal/vol34/iss11/13
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