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Application of Near Infrared Reflectance Spectroscopy-Radial Basis Function Neural Network for Non-destructive Determination of Coriolus Versicolor, Cordyceps Sinensis and Tricholoma Matsutake

Author: ZhangYiBo
Tutor: ZuoLiRong;MengQingFan;ZuoJiaHui
School: Jilin University
Course: Microbial and Biochemical Pharmacy
Keywords: Near-infrared diffuse reflectance spectroscopy RBF neural network Wavelet transform and wavelet packet transform Versicolor Cordyceps Matsutake Non-destructive analysis
CLC: R284
Type: Master's thesis
Year: 2008
Downloads: 164
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Abstract


Completed NIR spectroscopy combined with radial basis function neural network model , three medicinal fungi versicolor , Cordyceps and Matsutake active ingredients modeling analysis . After optimization of the pretreatment of the spectrum and radial basis function neural network model input parameters of the nodes , hidden nodes and the width of the radial basis (SC) , has been the best three medicinal fungal active ingredient in the near-infrared spectral analysis radial the base model : versicolor polysaccharide content analysis of optimal RBF neural network model the 6 scale decomposition WPT-NIRS-RBFNN (7-12-1,3.2), versicolor protein content analysis of the optimal radial basis the neural network model is 6 scale decomposition WPT-NIRS-RBFNN (12-10-1,3.0); 5 -scale of adenosine the optimal analysis model for wavelet packet WPT- NIRS - on RBFNN ( 6-6-1 , 3) , the model RMSECV 0.002541 , and the correlation coefficient for the model calibration set 0.901162 4 scale Cordyceps protein content analysis of the optimal model for wavelet packet WPT-NIRS-RBFNN (9-10-1,3.2) model RMSECV 0.013678 , correlation coefficient for the model calibration set 0.951925 ; the the the optimal analysis model of the matsutake protein RBF neural network model is a 5 -scale decomposition WPT- NIRS - on RBFNN ( 10-18-1,3.0 ) model RMSECV 0.00543 , the correlation coefficients of the model calibration set for 0.99335 matsutake polysaccharide content analysis of the optimal model for radial basis function neural network model is a 5 -scale decomposition WPT- NIRS - on RBFNN ( 12-10-1 , 2.6), the model RMSECV 0.00825 , the calibration set of the model correlation coefficient of 0.99055 . Good predictive performance of each model to meet the requirements of rapid non-destructive analysis .

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