Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging.

BACKGROUND: The quality of fresh tea leaves after harvest determines, to some extent, the quality and price of commercial tea. A fast and accurate method to evaluate the quality of fresh tea leaves is required. RESULTS: In this study, the potential of hyperspectral imaging in the range of 328-1115 nm for the rapid prediction of moisture, total nitrogen, crude fiber contents, and quality index value was investigated. Ninety samples of eight tea-leaf varieties and two picking standards were tested. Quantitative partial least squares regression (PLSR) models were established using a full spectrum, whereas multiple linear regression (MLR) models were developed using characteristic wavelengths selected by a successive projections algorithm (SPA) and competitive adaptive reweighted sampling. The results showed that the optimal SPA-MLR models for moisture, total nitrogen, crude fiber contents, and quality index value yielded optimal performance with coefficients of determination for prediction (R2 p) of 0.9357, 0.8543, 0.8188, 0.9168; root mean square error of 0.3437, 0.1097, 0.3795, 1.0358; and residual prediction deviation of 4.00, 2.56, 2.31, and 3.51, respectively. CONCLUSION: The results suggested that the hyperspectral imaging technique coupled with chemometrics was a promising tool for the rapid and nondestructive measurement of tea-leaf quality, and had the potential to develop multispectral imaging systems for future online detection of tea-leaf quality. © 2020 Society of Chemical Industry.

Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics.

BACKGROUND: Rapid and accurate diagnosis of nitrogen (N) status in field crops is of great significance for site-specific N fertilizer management. This study aimed to evaluate the potential of hyperspectral imaging coupled with chemometrics for the qualitative and quantitative diagnosis of N status in tea plants under field conditions. RESULTS: Hyperspectral data from mature leaves of tea plants with different N application rates were preprocessed by standard normal variate (SNV). Partial least squares discriminative analysis (PLS-DA) and least squares-support vector machines (LS-SVM) were used for the classification of different N status. Furthermore, partial least squares regression (PLSR) was used for the prediction of N content. The results showed that the LS-SVM model yielded better performance with correct classification rates of 82% and 92% in prediction sets for the diagnosis of different N application rates and N status, respectively. The PLSR model for leaf N content (LNC) showed excellent performance, with correlation coefficients of 0.924, root mean square error of 0.209, and residual predictive deviation of 2.686 in the prediction set. In addition, the important wavebands of the PLSR model were interpreted based on regression coefficients. CONCLUSION: Overall, our results suggest that the hyperspectral imaging technique can be an effective and accurate tool for qualitative and quantitative diagnosis of N status in tea plants. © 2019 Society of Chemical Industry.

[Rapid and Dynamic Determination Models of Amino Acids and Catechins Concentrations during the Processing Procedures of Keemun Black Tea].

Tea is one of the most popular beverages in the world. For the contribution to the taste and healthy functions of tea, amino acids and catechins are important components. Among different kinds of black teas in the world, Keemun black tea has the famous and specific fragrance, "Keemun aroma". During the processing procedure of Keemun black tea, the contents of amino acids and catechins changed greatly, and the differences of these concentrations during processing varied significantly. However, a rapid and dynamic determination method during the processing procedure was not existed up to now. In order to find out a rapid determination method for the contents of amino acids and catechins during the processing procedure of Keemun black tea, the materials of fresh leaves, withered leaves, twisted leaves, fermented leaves, and crude tea (after drying) were selected to acquire their corresponding near infrared spectroscopy and obtain their contents of amino acids and catechins by chemical analysis method. The original spectra data were preprocessed by the Standard Normal Variate Transformation (SNVT) method. And the model of Near Infrared (NIR) spectroscopy with the contents of amino acids and catechins combined with Synergy Interval Partial Least squares (Si-PLS) was established in this study. The correlation coefficients and the cross validation root mean square error are treated as the efficient indexes for evaluating models. The results showed that the optimal prediction model of amino acids by Si-PLS contained 20 spectral intervals combined with 4 subintervals and 9 principal component factors. The correlation coefficient and the root mean square error of the calibration set were 0. 955 8 and 1. 768, respectively; the correlation coefficient and the root mean square error of the prediction set were 0. 949 5 and 2. 16, respectively. And the optimal prediction model of catechins by Si-PLS contained 20 spectral intervals combined with 3 subintervals and 10 principal component factors. The correlation coefficient and the root mean square error of the calibration set were 0. 940 1 and 1. 22, respectively; the correlation coefficient and the root mean square error of the prediction set were 0. 938 5 and 1. 17, respectively. The results showed that the established models had good accuracy which could provide a theoretical foundation for the online determination of tea chemical components during processing.

Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models.

Near-infrared (NIR) spectroscopy is an effective tool for analyzing components relevant to tea quality, especially catechins and caffeine. In this study, we predicted catechins and caffeine content in green and black tea, the main consumed tea types worldwide, by using a micro-NIR spectrometer connected to a smartphone. Local models were established separately for green and black tea samples, and these samples were combined to create global models. Different spectral preprocessing methods were combined with linear partial-least squares regression and nonlinear support vector machine regression (SVR) to obtain accurate models. Standard normal variate (SNV)-based SNV-SVR models exhibited accurate predictive performance for both catechins and caffeine. For the prediction of quality components of tea, the global models obtained results comparable to those of the local models. The optimal global models for catechins and caffeine were SNV-SVR and particle swarm optimization (PSO)-simplified SNV-PSO-SVR, which achieved the best predictive performance with correlation coefficients in prediction (Rp) of 0.98 and 0.93, root mean square errors in prediction of 9.83 and 2.71, and residual predictive deviations of 4.44 and 2.60, respectively. Therefore, the proposed low-price, compact, and portable micro-NIR spectrometer connected to smartphones is an effective tool for analyzing tea quality.

Lignans from the heartwood of Streblus asper and their inhibiting activities to hepatitis B virus.

Three new lignans, erythro-strebluslignanol (1), threo-7'-methoxyl strebluslignanol (2) and erythro-7'-methoxyl strebluslignanol (3), together with twelve known compounds were isolated from the n-butanol and chloroform fractions of the heartwood of Streblus asper. Their structures were elucidated through extensive spectroscopic methods, including MS and 2D NMR experiments (HMQC and HMBC). The stereochemistry at the chiral center was determined using CD spectra, as well as analysis of coupling constants and optical rotation data, respectively. Primary bioassays showed that 6-hydroxyl-7-methoxyl-coumarin (5) and ursolic acid (10) showed anti-HBV activities, with IC(50) values of 29.60 µM and 89.91 µM for HBsAg at no cytotoxicity, and IC(50) values of 46.41 µM and 97.61 µM for HBeAg at no cytotoxicity, respectively.

Hypoglycemic effects and constituents of the barks of Cyclocarya paliurus and their inhibiting activities to glucosidase and glycogen phosphorylase.

The various fractions of the barks of Cyclocarya paliurus were systematically tested for hypoglycemic effects in alloxan-induced diabetic rats. The results showed that the chloroform fraction of the 75% EtOH extract of the barks of this plant exhibited significant blood sugar reducing activity, most of which were significantly higher than that of positive-drug metformin hydrochloride. A new compound, together with nine known compounds, was isolated from the most active fraction. The structure elucidation was based on spectroscopic methods, including two-dimensional NMR experiments (¹H-¹H COSY, HMQC, and HMBC). All of the isolates were evaluated for their α-glycosidase and glycogen phosphorylase inhibitory activities.