利用波长范围在833~2500nm的傅里叶变换近红外光谱(Fourier transform near infrared spectroscopy,FT-NIR)对不同霉变程度的玉米颗粒进行检测区分。首先,为避免光谱数据首尾噪声影响,对比四种常见的预处理方法,最终选择移动平均平滑法...利用波长范围在833~2500nm的傅里叶变换近红外光谱(Fourier transform near infrared spectroscopy,FT-NIR)对不同霉变程度的玉米颗粒进行检测区分。首先,为避免光谱数据首尾噪声影响,对比四种常见的预处理方法,最终选择移动平均平滑法对原始光谱数据进行预处理;然后为选出合适的样本集划分方法以提高模型预测性能,对常见的四种方法进行对比,最终利用SPXY(sample set partitioning based on joint x-y distance)法进行样本集划分;进一步为减少数据量,降低维度,使用连续投影算法(successive projections algorithm,SPA)提取出7个特征波长,分别为833,927,1208,1337,1454,1861和2280nm;最后,将七个特征波长数据作为输入,选取径向基函数(radial basis function,RBF)作为支持向量机(support vector machine,SVM)核函数,取参数C=7760469,γ=0.017003建立判别模型。SVM模型对训练集和测试集的预测准确率分别达到97.78%和93.33%。另取不同品种的玉米颗粒,以同样的标准挑选样品组成独立验证集,所建立的判别模型对独立验证集的预测准确率达到91.11%。结果表明基于SPA和SVM能有效地对玉米颗粒霉变程度进行判别,所选取的7个特征波长为实现在线霉变玉米颗粒近红外检测提供了理论依据。展开更多
Grain mildew is a significant hazard that causes food loss and poses a serious threat to human health when severe.Therefore,effective prediction and determination of mildew grade is essential for the prevention and co...Grain mildew is a significant hazard that causes food loss and poses a serious threat to human health when severe.Therefore,effective prediction and determination of mildew grade is essential for the prevention and control of mildew and global food security.In the present study,a model for predicting and determining the mildew grade of rice was constructed using logistic regression,back propagation neural network and GS-SVM(a grid search-based support vector machine algorithm)based on laboratory culture data and actual data from a granary,respectively.The results show that the GS-SVM model has a better prediction effect,but the model cannot automatically adjust the parameters and is more subjective,and the accuracy may decrease when the data set changes.Therefore,this paper establishes a new model for a support vector machine based on a fruit fly optimization algorithm(FOA-SVM),which can achieve automatic parameter search and automatically adjust its parameters to find the best result when the data set changes,with a strong ability of self-adjustment of parameters.In addition,the FOA-SVM converges quickly and the model is stable.The results of this study provide a technical method for early identification of mildew grade during grain storage,which is beneficial for the prevention and control of rice mildew during grain storage.展开更多
文摘利用波长范围在833~2500nm的傅里叶变换近红外光谱(Fourier transform near infrared spectroscopy,FT-NIR)对不同霉变程度的玉米颗粒进行检测区分。首先,为避免光谱数据首尾噪声影响,对比四种常见的预处理方法,最终选择移动平均平滑法对原始光谱数据进行预处理;然后为选出合适的样本集划分方法以提高模型预测性能,对常见的四种方法进行对比,最终利用SPXY(sample set partitioning based on joint x-y distance)法进行样本集划分;进一步为减少数据量,降低维度,使用连续投影算法(successive projections algorithm,SPA)提取出7个特征波长,分别为833,927,1208,1337,1454,1861和2280nm;最后,将七个特征波长数据作为输入,选取径向基函数(radial basis function,RBF)作为支持向量机(support vector machine,SVM)核函数,取参数C=7760469,γ=0.017003建立判别模型。SVM模型对训练集和测试集的预测准确率分别达到97.78%和93.33%。另取不同品种的玉米颗粒,以同样的标准挑选样品组成独立验证集,所建立的判别模型对独立验证集的预测准确率达到91.11%。结果表明基于SPA和SVM能有效地对玉米颗粒霉变程度进行判别,所选取的7个特征波长为实现在线霉变玉米颗粒近红外检测提供了理论依据。
基金the Special Funds for National Key Research and Development Program of China(No.2017YD0401005).
文摘Grain mildew is a significant hazard that causes food loss and poses a serious threat to human health when severe.Therefore,effective prediction and determination of mildew grade is essential for the prevention and control of mildew and global food security.In the present study,a model for predicting and determining the mildew grade of rice was constructed using logistic regression,back propagation neural network and GS-SVM(a grid search-based support vector machine algorithm)based on laboratory culture data and actual data from a granary,respectively.The results show that the GS-SVM model has a better prediction effect,but the model cannot automatically adjust the parameters and is more subjective,and the accuracy may decrease when the data set changes.Therefore,this paper establishes a new model for a support vector machine based on a fruit fly optimization algorithm(FOA-SVM),which can achieve automatic parameter search and automatically adjust its parameters to find the best result when the data set changes,with a strong ability of self-adjustment of parameters.In addition,the FOA-SVM converges quickly and the model is stable.The results of this study provide a technical method for early identification of mildew grade during grain storage,which is beneficial for the prevention and control of rice mildew during grain storage.
