摘要
将近红外光谱(NIRS)与三层径向基神经网络(RBFNN)结合,建立药用真菌云芝中活性成份多糖和蛋白的快速无损分析模型(NIRS-RBFNN)。采用卷积平滑、傅里叶变换、一阶变换、二阶变换、多尺度小波变换和小波包变换对原始光谱进行预处理。对处理后的光谱进行主成份的提取,以前15个主成份得分作为径向基神经网络的输入节点选择范围。对网络相关的参数(输入节点数、中间神经元数、径向基宽度常数)进行了优选。得到了最佳的云芝多糖分析模型的条件为:小波变换6尺度重构光谱,模型参数为WPT-NIRS-RBFNN(7-12-1,3.2),此时模型的交换验证方根误差(RMSECV)为0.009897,校正集相关系数Rcv=0.98357,此模型对预测集的预测远离方根误差(RMSEP)为0.00909,其相关系数Rp=0.98283;对云芝蛋白的最佳分析模型的条件为:对小波变换6尺度重构光谱,模型参数为WPT-NIRS-RBFNN(12-10-1,3.0),此时模型的RMSECV为0.005240,Rcv=0.99426,此模型对预测集的RMSEP为0.00998,Rp=0.98246。结果表明模型具有很好的稳健性和精确度。对实现药用真菌的无损快速分析有重要的意义。
A calibration model（NIRS-RBFNN） based on combination of near infrared reflectance spectroscopy and radial basis function neural network（RBFNN） has been proposed for synchronous and rapid non-destructive determination of Coriolus versicolor.Savitzky-Golay smoothing（SGS）,fast Fourier transform（FFT）,derivative of wavelet transformation（WT） and wavelet packet transformation（WPT） with multi-scale analysis were used to dispose the original NIRS,then principal component analysis（PCA） method is used to obtain the principal components（PC） scores.The anterior 15 PC scores were used as input data.These developed RBFNN have been optimized by selecting suitable parameters of input data,numbers of hidden layer neurons and spread constant through different pretreated spectra of calibration.The optimal quantitative analysis（NIRS-RBFNN） model for polysaccharide and protein of coriolus versicolor： for polysaccharide the optimal model is 6 scales reconstructed spectra of WPT,model parameter is WPT-NIRS-RBFNN（7-12-1,3.2）,and root mean squared error of cross validation（RMSECV） is 0.009897,Rcv=0.98357;root mean squared error of predictions（RMSEP） is 0.00909;Rp=0.98283.For Protein the optimal model is 6 scales reconstructed spectra of WPT,model parameter is WPT-NIRS-RBFNN（12-10-1,3.0）,and RMSECV is 0.00524,Rcv=0.99426;RMSEP is 0.00998;Rp=0.98246.These results show that the model has good robustness and precision and NIRS technology is convenient,rapid,no pretreatment and no pollution that this method could be popularized in the in situ measurement and the on-line quality control for coriolus versicolor.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2010年第12期3552-3557,共6页
Acta Optica Sinica
基金
中国医学基金会新药发展基金(20061108)资助课题
关键词
近红外光谱
径向基神经网络
云芝
无损分析
near infrared reflectance spectroscopy
radial basis function neural network
coriolus versicolor
non-destructive determination
