Quality Of Green Tea Research Articles

Microwave-Assisted Extraction Combined with Ultra-High-Performance Liquid Chromatography and Quadrupole/Q-Exactive High-Resolution Mass Spectrometry for the Determination of Main Flavor Substances in Green Tea.

In order to make up for the efficacy of sensory assessment of green tea quality and taste, main flavor substances should be quantitatively characterized based on efficient extraction and frontier determination scheme. A new, relatively simple sample preparation and detection workflow has been developed for the quantification and confirmation of flavor substances in green tea by microwave-assisted extraction combined with ultra-high-performance LC and quadrupole/Q-Exactive high-resolution MS system. The method identified main flavor substances (i.e., catechins, free amino acids, and caffeine) that were the main components in the formation of tea taste. After crushing pretreatment, tea was extracted and purified simultaneously in a microwave extraction jar by matrix-dispersed solid-phase extraction. The extracting solution was determined by LC coupled to high-resolution Orbitrap mass spectrometry. In this study, extraction solvent and purification materials were selected, extraction temperature and time were optimized, and the amount and proportion of solid-phase extraction packing were optimized. The method enabled detection of multiple catechins, amino acids, and caffeine in tea by LC coupled to high-resolution Orbitrap MS, and a database of tea flavor substances was established. The method could be used for screening and confirmation of the main flavor substances in tea as well as assisting the sensory evaluation to grade the quality of tea.

Effect of tannase on sediment formation in green tea infusion

Reducing sediment is important for the utilization and production of green tea infusion and concentrate. The effect of tannase on sediment formation in green tea infusion and concentrate was investigated. The results showed that the amount of sediment of green tea infusion significantly decreased after tannase treatment, which was due to the decrease in the concentration of gallated catechins. Significant positive correlations between the sediment amount and the gallated catechin (r = 0.980, p < 0.01) concentration were found. The formation of sediment in green tea infusion was significantly influenced by the tannase concentration and hydrolysis time. 0.5 mg/mL tannase concentration, 120 min hydrolysis time, and 35 °C hydrolysis temperature were found to be the optimized hydrolysis parameters for reducing the sediment. Tannase treatment not only lowered the amount of sediment of green tea concentrate (74.63%), but it also improved the taste quality of green tea concentrate after storage. These results could be used to improve the quality and utilization of green tea infusion and concentrate.

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms.

Tea trees are kept in shaded locations to increase their chlorophyll content, which influences green tea quality. Therefore, monitoring change in chlorophyll content under low light conditions is important for managing tea trees and producing high-quality green tea. Hyperspectral remote sensing is one of the most frequently used methods for estimating chlorophyll content. Numerous studies based on data collected under relatively low-stress conditions and many hyperspectral indices and radiative transfer models show that shade-grown tea performs poorly. The performance of four machine learning algorithms—random forest, support vector machine, deep belief nets, and kernel-based extreme learning machine (KELM)—in evaluating data collected from tea leaves cultivated under different shade treatments was tested. KELM performed best with a root-mean-square error of 8.94 ± 3.05 μg cm−2 and performance to deviation values from 1.70 to 8.04 for the test data. These results suggest that a combination of hyperspectral reflectance and KELM has the potential to trace changes in the chlorophyll content of shaded tea leaves.

Total Polyphenol, Rehydration Ratio, and Liquor Color of Different Grade Green Tea

Tea is a non-alcoholic beverage that is widely consumed after water with potential health benefits. In general, tea processing divided into three types, non-enzymatic oxidation process (green tea), semi-oxidation (oolong tea), and with enzymatic oxidation (black tea). The sorting and grading is a stage in controlling the quality of green tea. This study aims to determine the characteristics of different grades of green tea on the total polyphenols, liquor color, and rehydration ratio. The results showed that the pekoe/pecco grade had the highest total polyphenol and rehydration ratio compared to other grades. Pekoe grade has a greenish yellow and brighter liquor color. Application of good agriculture practice (GAP) and plucking of fresh tea leaves should be considered to obtain more grade pekoe in the green tea processing.

Nondestructive detection for moisture content in green tea based on dielectric properties and VISSA‐GWO‐SVR algorithm

Moisture content was an important indicator to measure quality of green tea. In order to detect moisture content in green tea effectively and accurately, nondestructive detection for moisture content in green tea based on dielectric technology was proposed in this paper. Inductance-capacitance-resistance (LCR) measuring instrument and coaxial-line cylinder capacitor were used to collect dielectric data. The characteristic frequency points were extracted by successive projection algorithm (SPA) and variable iterative space shrinkage approach (VISSA). Support vector regression (SVR) was used to establish prediction models based on full frequency points and characteristic frequency points. The model results demonstrated that VISSA-SVR model based on dielectric loss factor ε″ performed best among all the prediction models, but the prediction accuracy was not enough, so the gray wolf optimization (GWO) algorithm was introduced to optimize the parameters (c and g) in SVR model. Furthermore, the best prediction performances for detecting moisture content in green tea was obtained, with the determination coefficient and root mean square errors (RMSEs) for prediction were 0.9695 and 0.0602, respectively. Therefore, dielectric technology combined with VISSA-GWO-SVR model is feasible for nondestructive determination of the moisture content in tea, which will provide a promising tool for the moisture content detection of other agricultural products. Practical applications Well understanding moisture content in tea is great importance. The practical application of this paper is to develop a novel method for moisture content detection in tea using dielectric technology. Compared with traditional methods, dielectric technology can be used to detect moisture content in tea nondestructively and accurately. Characteristic frequency selection algorithms are used to remove the redundant information in the data, and optimization algorithm is used to improve the performance of the model. Thus, dielectric technology combined with the optimal model is considered the most promising method for detecting the moisture content in green tea.

Study on the main biochemical components of CH-1 and the suitability of green tea

In this experiment, CH-1 was used as material to study its main biochemical components and the suitability of green tea. The results showed that the content of the water extract, caffeine and tea polyphenols in CH-1 were significantly higher than those in FD, which were higher 0.44%~12.29%, 3.03%~7.05%, 11.39%~20.46%, respectively. A lower level of amino acid but a proper phenol-ammonia ratio of catechins in CH-1 contributed to a good quality of green tea made by CH-1, which was consistent with the results of sensory evaluation.

The surplus value of Azorean Camellia sinensis flowers as an important contributor affecting the nutraceutical benefits of green tea quality

The surplus value of Azorean Camellia sinensis flowers as an important contributor affecting the nutraceutical benefits of green tea quality

Stimulated biosynthesis of delphinidin-related anthocyanins in tea shoots reducing the quality of green tea in summer.

Greater proportions of purple tea buds and leaves usually appear in the summer, which seriously affects the color and taste quality of green tea products, yet the metabolism of purple tea shoots in summer remains unclear. Here, the metabolomic profiles and gene expression of related flavonoid metabolic pathways in the purple and normal green shoots of 'Longjing 43', and the quality of green tea made with these two phenotypes, were analyzed and compared. Differential metabolites identified using high-performance liquid chromatography-Orbitrap/mass spectrometry indicated that anthocyanin biosynthesis in purple leaves was enriched, with higher levels of anthocyanidins (delphinidin-hexose-coumaroyl showed the greatest increase), proanthocyanidins (oligomers of catechins) and kaempferol glycoside. Expression patterns of the genes ANR, ANS, FLS, LAR, C4H, PAL, CHI, CHS and DFR revealed that the metabolism of anthocyanin is positively regulated by high temperature and/or light levels in summer. Gas chromatography-mass spectrometry results showed that, in purple tea shoots, the metabolism of carbohydrates was enriched whereas that of amino acids was diminished, while their mannose, fructose, d-galactose, sorbose and d-glucose contents were more than double those found in green leaves. A sensory evaluation confirmed that a greater quantity of purple shoots had a greater negative impact on green tea quality because of a bitter taste and dark color (leaves and infusions were tested). These results highlight the need for and possibility of improving commercial tea quality via cultivation that controls the temperature or light of tea gardens during the summer. © 2019 Society of Chemical Industry.

Key Elements of Technology Package Influencing Annual Tea Production of Small Tea Growers (STGS): Insight from Biswanath District of India

Though the tea sector of Assam is primarily dominated by the estate sector comprising of large tea gardens, the relevance of small scale tea plantation can never be denied as nearly 30% of the state’s tea production is contributed by Small Tea Growers (STGs). In fact the quality of green tea leaves produced by the Small Tea Growers (STGs) has outclassed that of the tea leaves produced by the estate sector. Since tea plantation is a technology intensive practice, the growers are bound to adopt certain technology components like artificial fertiliser, artificial pesticide, HYV clone, artificial water supply facility, etc. All these elements constitute a technology package which helps the tea growers to reap greater benefit in tea cultivation. This study is a humble attempt to investigate the marginal impact of each element of the technology package adopted by the Small Tea Growers (STGs) on their annual green tea leave production. Moreover it also tries to figure out the jeopardised effect of the technology package used in tea cultivation on environment and health.

Characteristics of soil nutrients, heavy metals and tea quality in different intercropping patterns

Intercropping is an important agroforestry practice that can not only increase biodiversity, improve microenvironment and resource utilization, but also improve crop yield and quality. In this paper, the soil environmental characteristics and tea quality of three tea-fruit intercropping patterns (loquat-tea, waxberry-tea, and citrus-tea) and pure tea garden were studied. The soil samples of 0–10 cm, 10–20 cm and 20–30 cm depths in different seasons (spring and autumn) were collected to analyze the physicochemical characteristics of the soil. Corresponding tea bud samples in spring were collected for the determination of the main chemical components. The soil nutrient of the three intercropping patterns was higher than that of the single tea plantation, while the heavy metal content and pH value were opposite. Moreover, the difference in soil nutrient and pH under intercropping patterns were correlated with the sampling period. The tea plantation under intercropping patterns had higher free amino acid content, lower catechins content and the ratio of phenol to amino acids, which is conducive to the formation of green tea quality. Under the patterns of loquat-tea intercropping and citrus-tea intercropping, soil nutrients were higher than that of waxberry-tea intercropping. For the tea plantation soil heavy metals, waxberry-tea intercropping and citrus-tea intercropping had lower concentration than that of loquat-tea intercropping. However, citrus-tea intercropping is superior to the other two intercropping patterns for tea quality. These results may enhance the understanding of tea-fruit intercropping patterns for improving the soil environment of tea plantation and the quality of tea.

Quality Green Tea (Camellia sinensis L.) Clones Marked through Novel Traits

Tea clones and seed varieties released from Tocklai Tea Research Institutes are suitable primarily for manufacturing black tea (CTC/orthodox) while green tea manufactured from these clones are poor in quality. This led to identifying germplasm suitable for manufacturing green tea of high quality. Physiological parameters on net photosynthesis, transpiration, stomatal conductance, water-use efficiency, leaf temperature, and carboxylation efficiency of six selected germplasm (DH 1 and 2, DH 3 and 4, DH 5 and 6, DL 13, DL 25, and DL 39) were measured in first flush, second flush, rain flush, and autumn flush. Seasonal Yield, biochemical (L-theanine to total polyphenol ratio), and taster’s score were compared among seasons. Two protocols were used for green tea manufacturing, i.e., roasting and steaming. Significant differences in net photosynthesis (pn), stomatal conductance (gs), carboxylation efficiency (ci/ca), and yield were observed between flushes. Among the six test clones, the L-theanine to total polyphenol ratio found was highest in DH 5 and 6 in the roasting method. Structural and cluster analysis revealed that the DH clones genotypically occupied the same position as that of the popular green tea clone Longjing 43 of China and Yabukita of Japan, indicating suitability of these germplasm to develop new green tea clones. Clones DH 5 and 6 are suitable for green tea manufacturing and they can be released to the industry as new green tea clones. As far as manufacturing protocol is concerned, the roasting method of tea manufacturing was found superior over steaming.

Artificial neural network modeling and genetic algorithm optimization of process parameters in fluidized bed drying of green tea leaves

AbstractThe present study involved integration of artificial neural network (ANN) with genetic algorithm (GA) for predicting the optimized process parameters required for fluidized bed drying of green tea leaves. It had a layer each for input and output with linear activation function and two hidden layers with a sigmoid function. The feed forward back propagation method was used to train the developed model. The input parameters used by ANN for generalizing the drying process were temperature (50–80°C) and air flow velocity (7–9.5 m/s), and the output parameters were drying time, total color difference (TCD), and total phenolic content (TPC). The weights and bias values of trained ANN were used by GA to estimate the fitness function, which maximizes the TPC and minimizes the drying time and TCD. The optimum process condition of independent variables (80°C and 9 m/s) obtained from the hybrid ANN/GA was validated, and agreeable relationship between actual and predicted values with relative standard deviation (SD) of 5.7, 0.46, and 0.22 was found for drying time, TCD and TPC of dried leaves, respectively. Hence, under this optimal drying condition, best quality green tea can be obtained within the limits defined.Practical applicationsDrying is a popular unit operation being preferred to prolong shelf life of cash crops. Air drying is predominantly used at any given scale due to its economic feasibility. However, the quality parameters get compromised during the process of drying. Hence, an optimal drying condition is necessary to have dried product with intact nutritional and functional activity, alongside consumer acceptability. This was attempted using ANN and GA. Artificial neural network and genetic algorithm can estimate the optimum fluidized bed drying condition to have intact physical and chemical properties of green tea leaves. The results indicated that the developed ANN/GA drying model can efficiently estimate the values of quality parameters of dried green tea leaves, and also identify the optimal drying conditions for any new data.

Effects of Additive β-Cyclodextrin on the Performances of Green Tea Infusion

The effects of the added β-cyclodextrin (β-CD) in the tea infusion extraction on color quality of green tea (Camellia sinensis) infusion have been investigated in detail. Due to the added β-CD, the color of the tea infusion can be brightened effectively, and meanwhile, compared to conventional extraction, the retentions of tea polyphenols, catechins, and chlorophyll have also been proved to be increased greatly. Furthermore, the additive β-CD can also increase the viscosity of the tea infusion. In addition, the existing β-CD can lower the oxygen solubility ratio in tea infusions effectively, from which a high-quality tea infusion can be prepared ready for further processing.

Local tangent space alignment and relevance vector machine as nonlinear methods for estimating sensory quality of tea using NIR spectroscopy

The quality of green tea is conventionally tested by human sensory panel, who assign quality scores to different teas. In order to overcome the deficiency of sensory evaluation methods, near infrared (NIR) reflectance spectroscopy combined with nonlinear methods was used to evaluate the sensory quality of green tea in this study. Nonlinear dimension reduction methods such as Kernel Principal Component Analysis (KPCA), Sparse Principal Component Analysis (SPCA), and Local Tangent Space Alignment (LTSA) were used to reduce the dimension of the spectral data, and compared with linear dimension reduction methods such as Principal Component Analysis (PCA), Multidimensional Scaling (MDS). Relevance vector machine (RVM) as a nonlinear quantitative analysis method was performed comparatively to develop calibration models between the sensory scores of the sensory panels and objective determination (NIR spectra measurements), and compared with back-propagation artificial network and linear PLS model. Experiment results showed that the non-linear reduction algorithm LTSA could obtain better effect on spatial reduction dimensional for NIR spectral data of tea. And when the number K of the neighborhoods in LTSA was 15, the RMSECV of RVM model was the minimum. The performance of LTSA-RVM model was superior to the others when 8 intrinsic variables were used. In the prediction set, Rp, RMSEP and RPD were 0.963, 1.461 and 3.788, respectively. The absolute value of prediction error of all prediction samples in LTSA-RVM are below 3.0, which indicated that the sensory scores predicted by NIR were consistent with the scores by tea assessors. The results of recovery study were 98.42%–102.81% at 5 real tea samples and the relative error lower than 3%. The research demonstrates that NIR technology combined with nonlinear analysis methods can effectively estimate sensory quality of tea and also present a potential way for fast and effective determination of the sensory quality of green tea.

Synergetic quantitative composition-activity relationship between the aroma components and aroma quality of 8 famous green teas

This article identifies the aroma quality of eight different types and grades of famous green teas by E-nose combined with partial least squares discriminant analysis (PLSDA). Subsequently, in order to further explain the reasons for the differences in aroma quality, the background drift correction, multi-scale Gaussian smoothing and chromatographic retention time correction methods were employed to preprocess the chromatographic fingerprint of the famous green tea collected from GC-MS technology. Then, the pre-processed fingerprint and the famous green tea aroma score were analyzed to construct spectrum-activity relationship model by moving window partial least squares regression (MWPLSR). As a result, 21 kinds of latent characteristic aroma substances were screened out. Finally, the variable weighted least squares support vector (PSO-VWLS-SVM) was used to correlate the content of 21 kinds of latent characteristic aroma substances with the score for the aroma quality of different green tea. According to the contribution rate of each characteristic aroma substance to the score, a synergistic quantitative composition-activity relationship between aroma substances and the aroma quality of the famous green tea was successfully revealed. This method provided a new strategy for screening and quantitative analysis of characteristic aroma substances , as well their synergistic relationship in aroma quality of green tea.

Green tea: Authentication of geographic origin based on UHPLC-HRMS fingerprints

The quality of green tea is influenced by many factors, geographic origin being one of high importance, as this parameter is typically associated with tea quality grade. However, in some cases, fraudulent practices such as mislabelling of geographic origin may take place. In this pilot study, aimed at green tea authentication, we investigated whether metabolic fingerprinting based on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC–HRMS) enables sample classification to be achieved. In total, 54 authentic samples of green tea originating from China (n = 29), Japan (n = 17) and South Korea (n = 8) were available for experiments. To isolate even less polar metabolites, a dichloromethane/methanol (1:1. v/v) mixture was used for extraction. The data generated by the analysis of green tea extracts were processed by both unsupervised and supervised chemometric methods. The resulting predictive models document the applicability of this approach for green tea classification based on geographic origin.

Identification of a Novel Gene Encoding the Specialized Alanine Decarboxylase in Tea (Camellia sinensis) Plants.

Theanine, a unique amino acid in Camellia sinensis, accounts for more than 50% of total free amino acids in tea and has a significant contribution to the quality of green tea. Previous research indicated that theanine is synthesized from glutamic acid (Glu) and ethylamine mainly in roots, and that theanine accumulation depends on the availability of ethylamine which is derived from alanine (Ala) decarboxylation catalyzed by alanine decarboxylase (AlaDC). However, the specific gene encoding AlaDC protein remains to be discovered in tea plants or in other species. To explore the gene of AlaDC in tea plants, the differences in theanine contents and gene expressions between pretreatment and posttreatment of long-time nitrogen starvation were analyzed in young roots of two tea cultivars. A novel gene annotated as serine decarboxylase (SDC) was noted for its expression levels, which showed high consistency with theanine content, and the expression was remarkably high in young roots under sufficient nitrogen condition. To verify its function, full-length complementary DNA (cDNA) of this candidate gene was cloned from young roots of tea seedlings, and the target protein was expressed and purified from Escherichia coli (E. coli). The enzymatic activity of the protein for Ala and Ser was measured in vitro using ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS). The results illustrated that the target protein could catalyze the decarboxylation of Ala despite of its high similarity with SDC from other species. Therefore, this novel gene was identified as AlaDC and named CsAlaDC. Furthermore, the gene expression levels of CsAlaDC in different tissues of tea plants were also quantified with quantitative real-time PCR (qRT-PCR). The results suggest that transcription levels of CsAlaDC in root tissues are significantly higher than those in leaf tissues. That may explain why theanine biosynthesis preferentially occurs in the roots of tea plants. The expression of the gene was upregulated when nitrogen was present, suggesting that theanine biosynthesis is regulated by nitrogen supply and closely related to nitrogen metabolism for C. sinensis. The results of this study are significant supplements to the theanine biosynthetic pathway and provide evidence for the differential accumulation of theanine between C. sinensis and other species.

Gas chromatographic Analysis of Organic Acids in Japanese Green Tea Leaves.

Herein, gas chromatography is used to determine and quantify organic acids in Japanese green tea leaves, and the established method is employed to profile the acid components of Matcha, Gyokuro, Sencha green teas, and green tea varieties and thus acquire data needed to ensure the high quality and safety of green tea. The tea leaves were esterified with 10 vol% sulfuric acid in 1-butanol at 100℃ for 2 h. Oxalic acid contents were high in Asatsuyu and Okuyutaka samples and were low in Sofu, increasing in the order of Sencha < Gyokuro < Matcha, while citric acid content increased in the order of Sencha < Matcha < Gyokuro. Moreover, the oxalic acid content of Gyokuro only slightly increased with increasing tea grade. The relative contents of the different fatty acids did not strongly vary between the different green tea varieties. However, the n-3 to n-6 ratio was found to be low in Sofu. The progressing maturity increased the n-3 to n-6 ratio of Yabukita. The n-3 to n-6 ratio was low in high-grade Matcha, Gyokuro, Sencha green teas, and was related to the green tea quality. The developed method was concluded to be suitable for the evaluation of green tea quality.

Variation patterns in the content of glycosides during green tea manufacturing by a modification-specific metabolomics approach: Enzymatic reaction promoting an increase in the glycosidically bound volatiles at the pan firing stage

The glycosides are presumed to influence the quality of green tea but the molecular mechanism behind remains unclear. To elucidate the contribution of glycosides to the flavor formation of green tea, changes of both glycosidically bound non-volatiles (GBNVs) and glycosidically bound volatiles (GBVs) during the manufacturing of green tea were investigated using a modification-specific metabolomics method. A total of 64 glycosides (47 GBNVs and 17 GBVs) were identified and their contents mainly changed during the pan firing and drying stages of green tea manufacturing. Notably, the contents of GBVs significantly increased by 1.12–4.46-fold during pan firing. Correlation analysis showed that the GBVs contents were negatively related to the contents of volatiles and glucose. Model experiments revealed that enzymatic synthesis contributed to the increase in the content of GBVs during the pan firing. This comprehensive study on the glycosides changes revealed the molecular bases for GBVs increments during the pan firing.

Commodity characteristics and quality assessment of green tea sold in retail chains of Saint-Petersburg

Tea is one of the most favorite drinks in the world. About the useful effect of green tea on the human body is known very much. In connection with the fall of the economy, the quality of products entering the trading networks is also deteriorating, so we decided to check the quality of green tea sold by trading networks in St. Petersburg. There are the results of a study on the quality of green tea from different manufacturers in the article and accordance with their normative documents. Organoleptic and physico-chemical methods were used to allowable the quality of tea. We studied these samples for safety according to the maximum permissible content of toxins, radionuclides, mycotoxins and microorganisms in tea. According to the physico-chemical examination revealed violations in terms of: the content of tannin, water-soluble extractive substances, active acidity, increased mold content, which attests about assortment falsification of green tea, the use of poor quality raw materials, violation of technological processes and storage processes. As a result, it can be concluded that in production of tea it is necessary to take into account all regulatory requirements and comply with all technological processes, consider the requirements for storage and transportation of tea, certify the goods and use more manual labor when collecting tea leaves. Trading enterprises should use only certified goods and to use the conclusions of expert laboratoriesmore often.