Title :
Application of Wavelet Packet Transform-Radial Basis Function Neural Network in NIR Spectroscopy for Non-destructive Determination of Cordyceps Gunnii Mycelia Powder
Author :
Teng, Guo-sheng ; Wu, Yun-cheng ; Wang, Na-yi ; Wang, Ying-chao ; Gao, Leng ; Liu, Ya-hui
Author_Institution :
Sch. of Chem. & Life Sci., Changchun Univ. of Technol., Changchun, China
Abstract :
A novel calibration model has been proposed for synchronous, rapid and non-destructive determination the content of adenosine and protein in the Cordyceps gunnii mycelia powder by near infrared spectroscopy. This model is a combination of wavelet packet transform (WPT) data disposal with multi-scale analysis and radial basis function neural network (WPT-RBFNN). Via using principal component analysis (PCA) method for analyzing these reconstructed spectra matrix, the anterior 20 PC scores of principal components (PC) were obtained, which were used as input data in RBFNN. The network parameters including number of input nodes, number of hidden layer neurons and spread constant (SC) were investigated. WPT-RBFNN model which reconstructed the spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction is well optimized. The root mean square errors of prediction (RMSEP) for determination of adenosine and protein obtained from the optimum WPT-RBFNN model are 0.5987 and 0.0169, which are superior to those that obtained by the optimum RBFNN models with origin spectra. Regression coefficient (Rp) between NIR predicted values and actual values for adenosine and protein are 0.9385 and 0.9614. It is verified that WPT-RBFNN model with multi-scale analysis is a suitable approach to deal with NIR spectroscopy and model this complex non-linearity. The proposed method is convenient, rapid, no pretreatment and non-destructive.
Keywords :
fermentation; infrared spectra; mean square error methods; pharmaceutical industry; powders; principal component analysis; production engineering computing; proteins; quality control; radial basis function networks; regression analysis; wavelet transforms; Chinese medical fungal fermentation; Cordyceps Gunnii Mycelia powder; NIR spectroscopy; WPT data disposal; adenosine content; calibration model; hidden layer neuron; multiscale analysis; near infrared spectroscopy; nondestructive determination; principal component analysis; principal component score; protein content; quality control; radial basis function neural network; regression coefficient; root mean square errors-of-prediction; spread constant; wavelet packet transform; Calibration; Mathematical model; Neurons; Proteins; Spectroscopy; Wavelet packets; Cordyceps gunnii; near infrared spectroscopye; radial basis function neural network; wavelet packet transform;
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2012 Fifth International Conference on
Conference_Location :
Zhangjiajie, Hunan
Print_ISBN :
978-1-4673-0470-2
DOI :
10.1109/ICICTA.2012.35