Fast and nondestructive discrimination of quality level of Yongchuan Xiuya tea based on near infrared spectroscopy combined with artificial neural network algorithm

Abstract

Near infrared spectroscopy (NIRS) was used to discriminate the quality level of Yongchuan Xiuya tea quickly and nondestructively. Three quality levels of Yongchuan Xiuya tea were collected, then scanning NIRS, pretreating spectral noise information, screening characteristic spectral intervals by backward interval partial least squares, proceeding principal component analysis. Last, the jump connection nets artificial neural network (J-BP-ANN) with three kinds of transfer functions was applied to establish models. The best pretreated method was the combination of multivariate scattering correction and the first derivative. Six characteristic spectral intervals were screened, which accounting for 27.23% spectral data. The cumulative contribution rate of the first three principal components of the selected characteristic spectra was 97.85%. When the J-BP-ANN calibration set model was established with the tanh function, NIRS model had the best results, whose root mean square error and determination coefficient of the cross validation were 0.953 and 0.031, respectively. The root mean square error and the determination coefficient of the prediction set model were 0.942 and 0.041, respectively. The absolute deviation values of prediction set samples were <0.08. The results showed NIRS can predict the quality levels of Yongchuan Xiuya tea quickly and accurately.