Discrimination of varieties of paddy based on Vis/NIR spectroscopy combined with chemometrics

A simple, fast and non-destructive method based on visible/near infrared reflectance (Vis/NIR) spectroscopy and chemometrics was put forward for discriminating varieties of paddy. Firstly, A field spectroradiometer was used for collecting spectra in the wavelength range from 325 to 1 025 nm. The Vis/NIR spectra were acquired from 150 samples of five varieties of paddy. Secondly, original spectral data were decomposed as low-frequency wavelet coefficients and high-frequency wavelet coefficients by wavelet transform (WT) at first level. High-frequency wavelet coefficients were deleted as they contained too many noise, so the reconstructed signals from low-frequency wavelet coefficients were used as replacer of original spectral data. Thirdly, principal component analysis (PCA) compressed the above data into several new variables that were the linear combination of original spectral variables. The analysis suggested that the first four PCs (principle components) could account for 99.89% of the original spectral information, it means that the four PCs could explain most variation of original variables. In order to set up the model for discriminating varieties of paddy, the four diagnostic PCs were applied as inputs of back propagation artificial neural network (BP-ANN), and the values of varieties of different paddy were applied as the outputs of BP-ANN. The threshold of error was set as 0.2, the optimal structure of BP-ANN was three layers with nodes as 4-9-3. The whole 150 samples were randomly divided into two parts, one of which that consisted of 100 samples was used to model, and the other one containing 50 samples was used to predict. This model has been used to predict the varieties of 50 unknown samples, and the discrimination rate 96% has been obtained. It proved that the model was very reliable and practicable. In short, it is feasible to discriminate varieties of paddy based on visible/near infrared reflectance (Vis/NIR) spectroscopy and chemometrics.