The current developmental trend is to evaluate the quality of Yongchuan Xiuya tea rapidly. After spectrum pre-processing, near infrared spectroscopy (NIRS) coupled with synergy interval partial least squares (siPLS), principal component analysis (PCA) and back propagation-artificial neural network (BP-ANN) was applied to rapidly and non-destructively predict the quality of Yongchuan Xiuya tea. External Yongchuan Xiuya tea samples were used for the actual application of the proposed model. The best pre-processing method was multiple scattering correction coupled with second derivative, and the characteristic spectral regions selected by siPLS were 4381.5-4755.6 cm -1 , 4759.5-5133.6 cm -1 , 6266.6-6637.8 cm -1 and 7389.9-7760.2 cm -1 . The cumulative contribution rate was 99.05% for the first three principal components of the characteristic spectra regions. The transfer function, root mean square error and determinant coefficient of the best BP-ANN prediction model were the tanh function, 0.384 and 0.977, respectively. The root mean square error and determinant coefficient of the external 10 Yongchuan Xiuya tea samples were 0.406 and 0.969, respectively. These results showed that NIRS combined with BP-ANN algorithm can be used to evaluate the quality of Yongchuan Xiuya tea rapidly and accurately.
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.
Near infrared spectroscopy (NIRS) combined with multiple algorithms was used to determinate the tea polyphenols content in Chongzhou new loquat tea lines quickly and nondestructively. Samples of 26 Chongzhou new loquat tea lines were collected, then scanning NIRS, pretreating spectral noise information, screening characteristic spectral intervals by backward interval partial least squares, proceeding principal component analysis. Finally, the artificial neural network (BP-ANN) method with three kinds of transfer functions was applied to establish models. The best pretreated method was the combination of standard normal variation (SNV) and first derivative, and the characteristic spectral regions selected were 4381.5-4755.6 cm -1 , 4759.5-5133.6 cm -1 , 6266.6-6637.8 cm -1 and 7389.9-7760.2 cm -1 , respectively. The cumulative contribution rate of the first three principal components of the selected characteristic spectra was 95.24%. When the BP-ANN calibration set model was established with the logistic function, NIRS model had the best results, whose root mean square error and determination coefficient of the cross validation were 0.975 and 0.372%, respectively. The root mean square error and the determination coefficient of the prediction set model were 0.962 and 0.400%, respectively. The results showed NIRS can predict the tea polyphenols content in Chongzhou new loquat tea lines quickly and accurately.
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