Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model Original Research Article

Visible/near-infrared spectroscopy (NIRS), with the characteristics of high speed, non-destructiveness, high precision and reliable detection data, etc., is a pollution-free, rapid, quantitative and qualitative analysis method. A new approach for discrimination of varieties of tea by means of vis/NIR spectroscopy (325–1075 nm) was developed in this work. The relationship between the reflectance spectra and tea varieties was established. The spectral data was compressed by the wavelet transform (WT). The features from WT can be visualized in principal component (PC) space, which can lead to discovery of structures correlative with the different class of spectra samples. It appeared to provide a reasonable clustering of the varieties of tea. The scores of the first eight principal components computed by PCA had been applied as inputs to a back propagation neural network with one hidden layer. The 200 samples of eight varieties were selected randomly to build BP-ANN model. This model was used to predict the varieties of 40 unknown samples. The recognition rate of 100% was achieved. This model comes to be reliable and practicable.