Application Of Near Infrared Reflectance Spectroscopy-radial Basis Function Neural Network For Rapid Non-destructive Determination Of Ganoderma Lucidum

Along with the modern science technology's swift development, the analysis work's meter mechanized, the automation to obtain the unprecedented development, the request drug analysis toward the green, fast, highly effective and so on directions has also developed. Near infrared spectroscopy technology (NIRS) is a novel technology applying to rapid determination of single component or multicomponents in materials depend on their optical characteristic in near infrared spectroscopy region. The equipment now analysts can rapidly and precisely acquire a large amount of measured data. As the NIRS is very complicate and serious overlap, it is difficult to quantitative analyze multi-components sample depend on the height of peaks in spectra, however, the analysts have to face this problem: how to extract more meaningful chemical information from the measured data, it is necessary to apply a chemometric to develop a multiple regression quantitative model using for analysis and prediction.The radial basis function neural network (RBFNN) has been developed for quantitative analysis of drugs during the last decade. Because of its powerful nonlinear projection ability and local approximation, it is very suitable for the solution of the inherent relationship from mass. The topologic structure of a typical RBF networks is characterized by input nodes number, spread constant and hidden layer neurons number.Wavelet Packet Transform (WPT) has been proved to be a powerful tool for compressing analytical data. It transforms the raw measured data into the wavelet domain. The information contained in the raw data can be represented by the wavelet coefficients. Because of the WT property, there are many wavelet coefficients with very small amplitude, which can be regarded as uninformative; these can be removed without substantially affecting the useful information. WPT is the generalization of WT, it can decompose the data with approximation coefficient and detail coefficient with different scales for a more precise signal extraction。In this paper, I have studied on the application of NIRS combination with RBFNN on the active constituent of Ganoderma lucidum for non-destruction determination. And then I studied on the influence of pretreatment methods of NIRS spectra with Wavelet Packet Transform (WPT) on establishing quantitative analysis model, discussed the optimal method and opportune factors influences in the models.The optimal quantitative analysis model for Protein and Polysaccharide of Ganoderma lucidum Polysaccharide the optimal model is 5 scales decompose of WPT, WPT-NIRS-RBFNN(10-18-1,SC=2.6),and RMSECV is 0.00825, Rc is 0.9925; RMSEP is 0.00976;Rp is 0.98283。For Protein the optimal model is 5 scales decompose of WPT, WPT-NIRS-RBFNN(12-10-1,3.0),and RMSECV is 0.00548, Rc is 0.9956; RMSEP is 0.00835;Rp is 0.9826。Each ingredient's optimization model's reproduction quality and the returns-ratio experimental result further indicated that this method is good to for medicinal purposes fungus active constituent's modelling determination reproduction quality, the returns-ratio is high. In the experiment simultaneously had also indicated the WPT in spectrum pretreatment method has under the suitable scale compared to a convention processing method more superior effect. The results indicate that it is feasible to apply NIRS combine with RBFNN to non-destruction determination of the active constituent of Ganoderma lucidum. It can be generalized to on-line and real-time quality control in pharmacy.