Near Infrared Multi-component Prediction Model Based on Principal Component Analysis and Wavelet Neural Network

A new near infrared model for multi-component prediction was established based on principal component analysis (PCA) and wavelet neural network (WNN) methods. First, original near infrared spectra from wheat leaves were pre-processed, and their principal components were extracted by PCA, which could reduce the dimensionality of spectrum data. The first three principal components were taken as inputs of wavelet neural network (WNN), and the influence of neuron number in the hidden layer of WNN on the properties of model was further analyzed. The results indicated that the WNN model can be applied to the simultaneous determination of total nitrogen and soluble sugar contents of wheat dry leaves. The root mean square errors of prediction (RMSEP) of total nitrogen and soluble sugar contents of wheat leaves by the WNN model were 0.10% and 0.09%, with correlation coefficients(R) of 0.980 and 0.967, respectively. In addition, the WNN model was superior to the methods of back-propagation neural network (BPNN) and partial least squares (PLS) on convergence speed and prediction precision. This study provided an approach for quantitative analysis of multi-components based on near infrared spectrum.