基于高光谱融合神经网络的玉米黄曲霉毒素B1和赤霉烯酮含量预测

为了消除散射对高光谱信息的影响采用多元散射校正(multiplicative scatter correction,MSC)处理原始光谱;根据相关系数法选择有效波段,通过连续投影算法结合信息熵选择8个特征波长;建立有效波段和不同特征波长下的霉变玉米黄曲霉毒素B_1与赤霉烯酮含量的BP神经网络预测模型。结果表明:8个特征波长下光谱信息所建立的预测模型最佳,黄曲霉毒素B1含量预测正确率为98.74%,均方根误差为0.048 5;赤霉烯酮含量预测正确率为100%,均方根误差为0.160 5。因此高光谱融合神经网络检测霉变玉米黄曲霉毒素B1及赤霉烯酮含量具有可行性。

Detection of moldy maize aflatoxin B1 and gibberellinby hyperspectral coupled with neural network

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.