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《Chinese Journal of Analytical Chemistry》 2008-06
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A Method of Near Infrared Multi-component Analysis Based on Independent Component Analysis and Neural Networks Model

FANG Li-Min,LIN Min(College of Metrology Technology and Engineering,China Jiliang University,Hangzhou 310018)  
A new method of model construction based on wavelet transform(WT),back-propagation artificial neural networks(BP-ANN)regression and independent component analysis(ICA)was proposed.In its application to near infrared spectrum,the data of NIR spectrum were firstly compressed by wavelet transform,their independent components and the mixing matrix were then extracted by the independent component analysis,and finally,the model of the maxing matrix and concentration matrix was built by the artificial neural networks regression.The influence of the numbers of independent components and the neurons in the hidden layer on the properties of model was further analysed.Compared to the model made by direct use of spectrum matrix as input of the neural network,ICA-NNR(neural network regression)model has advantages in both the correlation coefficient and root mean square error indicators.This new chemometric method has been applied to the simultaneous determination of three components(moisture,protein,starch)of corn samples.The correlation coefficients(r)between the chemically tested values and the near-infrared method predicted values of starch,protein and moisture are 0.971,0.976 and 0.975 respectively.
【Fund】: 浙江省科技厅重点项目(No.2006C21044)资助
【CateGory Index】: O657.33
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