High-speed identification system for fresh tea leaves based on phenotypic characteristics utilizing an improved genetic algorithm.

BACKGROUND: High-quality tea requires leaves of similar size and tenderness. The grade of the fresh leaves determines the quality of the tea. The automated classification of fresh tea leaves improves resource utilization and reduces manual picking costs. The present study proposes a method based on an improved genetic algorithm for identifying fresh tea leaves in high-speed parabolic motion using the phenotypic characteristics of the leaves. During parabolic flight, light is transmitted through the tea leaves, and six types of fresh tea leaves can be quickly identified by a camera. RESULTS: The influence of combinations of morphology, color, and custom corner-point morphological features on the classification results were investigated, and the necessary dimensionality of the model was tested. After feature selection and combination, the classification performance of the Naive Bayes, k-nearest neighbor, and support vector machine algorithms were compared. The recognition time of Naive Bayes was the shortest, whereas the accuracy of support vector machine had the best classification accuracy at approximately 97%. The support vector machine algorithm with only three feature dimensions (equivalent diameter, circularity, and skeleton endpoints) can meet production requirements with an accuracy rate reaching 92.5%. The proposed algorithm was tested by using the Swedish leaf and Flavia data sets, on which it achieved accuracies of 99.57% and 99.44%, respectively, demonstrating the flexibility and efficiency of the recognition scheme detailed in the present study. CONCLUSION: This research provides an efficient tea leaves recognition system that can be applied to production lines to reduce manual picking costs. © 2022 Society of Chemical Industry.

α-Glucosidase Inhibitory Activities and the Interaction Mechanism of Novel Spiro-Flavoalkaloids from YingDe Green Tea.

Flavoalkaloids are a unique class of compounds in tea, most of which have an N-ethyl-2-pyrrolidinone moiety substituted at the A ring of a catechin skeleton. 1-Ethyl-5-hydroxy-pyrrolidone, a decomposed product of theanine, was supposed to be the key intermediate to form tea flavoalkaloids. However, we have also detected another possible theanine intermediate, 1-ethyl-5-oxopyrrolidine-2-carboxylic acid, and speculated if there are related conjugated catechins. Herein, four novel spiro-flavoalkaloids with a spiro-γ-lactone structural moiety were isolated from Yingde green tea (Camellia sinensis var. assamica) in our continuing exploration of new chemical constituents from tea. The structures of the new compounds, spiro-flavoalkaloids A-D (1-4), were further elucidated by extensive nuclear magnetic resonance (NMR) spectroscopy together with the calculated 13C NMR, IR, UV-vis, high-resolution mass, optical rotation, experimental, and calculated circular dichroism spectra. We also provided an alternative pathway to produce these novel spiro-flavoalkaloids. Additionally, their α-glucosidase inhibitory activities were determined with IC50 values of 3.34 (1), 5.47 (2), 22.50 (3), and 15.38 (4) µM. Docking results revealed that compounds 1 and 2 mainly interacted with residues ASP-215, ARG-442, ASP-352, GLU-411, HIS-280, ARG-315, and ASN-415 of α-glucosidase through hydrogen bonds. The fluorescence intensity of α-glucosidase could be quenched by compounds 1 and 2 in a static style.

Untargeted Metabolomics Combined with Bioassay Reveals the Change in Critical Bioactive Compounds during the Processing of Qingzhuan Tea.

Qingzhuan tea (QZT) is a typical Chinese dark tea that has a long-time manufacturing process. In the present study, liquid chromatography coupled with tandem mass spectrometry was used to study the chemical changes of tea samples during QZT processing. Untargeted metabolomics analysis revealed that the pile-fermentation and turnover (post-fermentation, FT) was the crucial stage in transforming the main compounds of QZT, whose contents of flavan-3-ols and flavonoids glycosides were decreased significantly. The bioactivities, including the antioxidant capacities and inhibitory effects on α-amylase and α-glucosidase, were also reduced after the FT process. It was suggested that although the QZT sensory properties improved following pile-fermentation and aging, the bioactivities remained restrained. Correlation analysis indicated that the main galloylated catechins and flavonoid glycosides were highly related to their antioxidant capacity and inhibitory effects on α-amylase and α-glucosidase.

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.

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.

Large scale preparation, stress analysis, and storage of headspace volatile condensates from Jasminum sambac flowers.

In this study, a large-scale preparation of jasmine floral volatile condensate (FVC) was conducted using fresh flowers without any extraction solvent involvement. Condensate volatile profiles were compared to those of fresh flowers for their scent characteristics and ability to withstand manufacturing and storage. The FVC possessed a typical jasmine flower scent, a similar odor polygon shape and greatly enhanced odor intensity and character odorants linalool, indole, and methyl anthranilate. In late August and September in Fuzhou, China, the ratio of odor activity values for indole/linalool in FVCs was close to that of fresh flowers, indicating that these were suitable local harvest times for FVC preparation. Room temperature storage for 30 months dramatically reduced the abundance of potent odorants and FVC scent intensity, while cold temperature (4 °C) storage was able to maintain FVC clarity and scent intensity. Our findings should be helpful at improving FVC quantity, quality, and storage.

[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.

[Identification of Pu'er teas with different storage years by FTIR spectroscopy].

Infrared spectra of Pu'er raw tea and Pu'er ripe tea were investigated using Fourier transform spectroscopy, in order to exploit a rapid method for discrimination of aging period for Pu' er tea samples. The results showed that the two kinds of Pu'er teas shared a similar woveform of infrared spectrum. However, due to the variations of aging time, leading to different chemical composition in pu'er teas, both Pu'er raw tea and ripe tea displayed corresponding different characteristic peaks. And the extent of aging of Pu'er tea had a significant relationship with optical density and waveforms of absorption peaks in the wave number range of 1 120-1 570 cm(-1) and 400-853 cm(-1), suggesting that the extent of aging of Pu'er tea may be identified by infrared spectrum technology rapidly and simply.