Content Of Tea Polyphenols Research Articles

Antioxidants improve β-cypermethrin degradation by alleviating oxidative damage and increasing bioavailability by Bacillus cereus GW-01

Microbial degradation of pesticide residues has the potential to reduce their hazards to human and environmental health. However, in some cases, degradation can activate pesticides, making them more toxic to microbes. Here we report on the β-cypermethrin (β-CY) toxicity to Bacillus cereus GW-01, a recently described β-CY degrader, and effects of antioxidants on β-CY degradation. GW-01 exposed to β-CY negatively affected the growth rate. The highest maximum specific growth rate (μm) appeared at 25 mg/L β-CY. β-CY induced the oxidative stress in GW-01. The activities of superoxide dismutase (SOD), catalyse (CAT), and glutathione-S-transferase (GST) were significantly higher than that in control (p < 0.01); but they are decreased as growth phase pronged, which is contrary to the β-CY degradation by GW-01 cells obtaining from various growth phase. Ascorbic acid (Vc), tea polyphenols (TP), and adenosine monophosphate (AMP) improved the degradation through changing the physiological property of GW-01. TP and AMP prompted the expression of gene encoding β-CY degradation in GW-01, while Vc does the opposite. Biofilm formation was significantly inhibited by β-CY, while was significantly enhanced by certain concentrations of TP and AMP (p < 0.05); while cell surface hydrophobicity (CSH) was negatively associated with β-CY concentrations from 25 to 100 mg/L, and these 4 antioxidants all boosted the CSH. Cells grown with β-CY had lower levels of saturated fatty acids but increased levels of some unsaturated and branched fatty acids, and these antioxidants alleviated the FA composition changes and gene expression related with FA metabolism. We also mined transcriptome analyses at lag, logarithmic, and stationary phases, and found that β-CY induced oxidative stress. The objective of this study was to elaborate characteristics in relation to the microbial resistance of pesticide poisoning and the efficiency of pesticide degradation, and to provide a promising method for improving pesticide degradation by microbes.

Selective galena depression mechanism of tea polyphenol in chalcopyrite flotation

In this study, the selective galena depression mechanism of tea polyphenol (TP) was investigated in chalcopyrite flotation. The feasibility of using TP as a galena depressant was studied through flotation experiments, and the depressant mechanism of TP on galena was analyzed using contact angle measurements, zeta potential measurements, adsorption experiments, X-ray photoelectron spectroscopy (XPS) analysis, and time-of-flight secondary ion mass spectrometry (ToF-SIMS) tests. Artificially mixed mineral flotation tests were performed at a pH of 8, TP concentration of 4 × 10–4 mol/L, and SIX concentration of 2 × 10–4 mol/L, resulting in a copper concentrate with a recovery of 83.07 % and a copper grade of 29.32 %. The lead recovery and grade of the copper concentrate were 8.87 % and 7.87 %, respectively, demonstrating the feasibility of using TP as a galena depressant. The zeta potential measurements and adsorption experiments showed that the adsorption of SIX on the surface of chalcopyrite was less affected by the TP treatment, while the TP significantly hindered the adsorption of SIX on the surface of galena, which the galena ToF-SIMS test results further support.

Synchronously Predicting Tea Polyphenol and Epigallocatechin Gallate in Tea Leaves Using Fourier Transform-Near-Infrared Spectroscopy and Machine Learning.

Tea polyphenol and epigallocatechin gallate (EGCG) were considered as key components of tea. The rapid prediction of these two components can be beneficial for tea quality control and product development for tea producers, breeders and consumers. This study aimed to develop reliable models for tea polyphenols and EGCG content prediction during the breeding process using Fourier Transform-near infrared (FT-NIR) spectroscopy combined with machine learning algorithms. Various spectral preprocessing methods including Savitzky-Golay smoothing (SG), standard normal variate (SNV), vector normalization (VN), multiplicative scatter correction (MSC) and first derivative (FD) were applied to improve the quality of the collected spectra. Partial least squares regression (PLSR) and least squares support vector regression (LS-SVR) were introduced to establish models for tea polyphenol and EGCG content prediction based on different preprocessed spectral data. Variable selection algorithms, including competitive adaptive reweighted sampling (CARS) and random forest (RF), were further utilized to identify key spectral bands to improve the efficiency of the models. The results demonstrate that the optimal model for tea polyphenols calibration was the LS-SVR with Rp = 0.975 and RPD = 4.540 based on SG-smoothed full spectra. For EGCG detection, the best model was the LS-SVR with Rp = 0.936 and RPD = 2.841 using full original spectra as model inputs. The application of variable selection algorithms further improved the predictive performance of the models. The LS-SVR model for tea polyphenols prediction with Rp = 0.978 and RPD = 4.833 used 30 CARS-selected variables, while the LS-SVR model build on 27 RF-selected variables achieved the best predictive ability with Rp = 0.944 and RPD = 3.049, respectively, for EGCG prediction. The results demonstrate a potential of FT-NIR spectroscopy combined with machine learning for the rapid screening of genotypes with high tea polyphenol and EGCG content in tea leaves.

Cross-cultivar prediction of quality indicators of tea based on VIS-NIR hyperspectral imaging

The rapid detection of quality indicators of fresh tea leaves is helpful to guide tea garden management. Therefore, it is very important to develop a fast and reliable detection method for fresh tea leaf quality indicators. In this study, the feasibility of visible-near-infrared hyperspectral image (VNHI) combined with stoichiometry for the determination of tea polyphenols (TP) and crude fiber (CF) in 14 cultivars of tea plants was investigated. Based on the VNHI, different tea cultivars were distinguished with an accuracy of 99.56% by using one-dimensional ResNet18 (1D-ResNet18). For the quantitative determination of CF and TP contents, the prediction determination coefficient (r2) reached 0.80 and 0.77, respectively, by integrating VNHI with PLS. In addition, we proposed a generalization index (GI) that can evaluate the cross-cultivar generalization ability of models. The GI can judge if the model built on a few tea cultivars can predict the quality indicators of other cultivars. Then, the predicted distribution maps of CF and TP contents were analyzed. The results showed that the quantitative models based on spectra (474–1734 nm) had higher cross-cultivar generalization ability with higher mean GI_0.7 in predicting the cross-cultivar content of CF (mean GI_0.7 = 2.563) and TP (mean GI_0.7 = 1.376). The models based on spectra (474–1030 nm) had stronger visualization ability with lower mean predictive variance. This study proved the feasibility of the VNHI technique in predicting the content of TP and CF of multiple cultivars of tea plants and provided methods with higher generalization, perceptual intuition, and speediness for the detection of quality indicators of tea in the field.

Tea Polyphenols Inhibit the Activity and Toxicity of Staphylococcus aureus by Destroying Cell Membranes and Accumulating Reactive Oxygen Species.

Staphylococcus aureus can cause bacterial food intoxication and seriously affect human health. Tea polyphenols (TP) are a kind of natural, safe, and broad-spectrum bacteriostatic substances, with a wide range of bacteriostatic effects. In the study, we explored the possible bacteriostatic mode of TP. The minimum inhibitory concentration of TP against S. aureus was 64 μg/mL. Protein, DNA, and K+ leak experiments, fluorescence microscopy, and transmission electron microscopy suggested that TP disrupt cell membranes, leading to intracellular component loss. By studying the effect of TP on the toxicity of S. aureus, it was found that the expression levels of two toxin genes, coa and spa, were downregulated by 2.37 and 32.6, respectively. Furthermore, after treatment with TP, a large number of reactive oxygen species (ROS) were propagated and released, leading to oxidative stress in cells. We speculated that the bacteriostatic mechanism of TP may be through the destruction of the cell membrane and ROS-mediated oxidative stress. Meanwhile, the hemolysis activity proved the safety of TP. Our results suggested that TP may be a potential antimicrobial agent for food.

Zinc Oxide Nanoparticles Enhanced Growth of Tea Trees via Modulating Antioxidant Activity and Secondary Metabolites

Nano-fertilizer has been dubbed ‘the fertilizer of the 21st century’, and it is already being used extensively in agriculture. Zinc oxide nanoparticles (ZnO-NPs) have excellent biological properties and are expected to be an ideal choice for plant zinc fertilizer. Tea is one of the top three beverages in the world, and improving the quality of tea is a priority in its research field. In this study, different concentrations (0, 10, 50, 100, 150, and 200 mg·L−1) of ZnO-NPs were sprayed on tea leaves to investigate their effects on volatile aroma substances and biochemical aspects of tea leaves. The results revealed that various concentrations of ZnO-NPs had different effects on physiological indexes. The concentration of 150 mg·L−1 of ZnO-NPs enhanced chlorophyll content, while the 100 mg·L−1 concentration of ZnO-NPs promoted the accumulation of soluble proteins and the activity of antioxidant enzymes, including a decrease in the content of malondialdehyde. In addition, the ZnO-NPs spray reduced the content of tea polyphenols. A total of 27 volatiles were identified under six treatments, with benzene being the common compound with an average content of 45.97%. Ethanolamine and cis-3-hexenyl acetate were the other two major compounds. It was concluded that the presence of ZnO-NPs improved the antioxidant system of teas, increased soluble protein content and provided better reactive oxygen species protection for plants, especially in the case of ZnO-NPs at 100 mg·L−1. We highlighted that ZnO-NPs application was a favorable way to improve tea trees growth.

A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea.

To investigate different contents of pu-erh tea polyphenol affected by abiotic stress, this research determined the contents of tea polyphenol in teas produced by Yuecheng, a Xishuangbanna-based tea producer in Yunnan Province. The study drew a preliminary conclusion that eight factors, namely, altitude, nickel, available cadmium, organic matter, N, P, K, and alkaline hydrolysis nitrogen, had a considerable influence on tea polyphenol content with a combined analysis of specific altitudes and soil composition. The nomogram model constructed with three variables, altitude, organic matter, and P, screened by LASSO regression showed that the AUC of the training group and the validation group were respectively 0.839 and 0.750, and calibration curves were consistent. A visualized prediction system for the content of pu-erh tea polyphenol based on the nomogram model was developed and its accuracy rate, supported by measured data, reached 80.95%. This research explored the change of tea polyphenol content under abiotic stress, laying a solid foundation for further predictions for and studies on the quality of pu-erh tea and providing some theoretical scientific basis.

Study on the Trend in Microbial Changes during the Fermentation of Black Tea and Its Effect on the Quality.

The role of tea endophytes in black tea fermentation and their impact on black tea quality remain unclear. We collected fresh leaves of Bixiangzao and Mingfeng tea and processed them into black tea, while testing the biochemical composition of both the fresh leaves and the black tea. We also used high-throughput techniques, such as 16S rRNA, to analyze the dynamic changes in the microbial community structure and function during black tea processing in order to investigate the influence of dominant microorganisms on the quality of black tea formation. Our results showed that bacteria, such as Chryseobacterium and Sphingomonas, and Pleosporales fungi dominated the entire black tea fermentation process. Predicted functional analysis of the bacterial community indicated that glycolysis-related enzymes, pyruvate dehydrogenase, and tricarboxylic acid cycle-related enzymes were significantly elevated during the fermentation stage. Amino acids, soluble sugars, and tea pigment content also increased considerably during fermentation. Pearson's correlation analysis revealed that the relative bacterial abundance was closely related to the content of tea polyphenols and catechins. This study provides new insights into the changes in microbial communities during the fermentation of black tea and demonstrates understanding of the basic functional microorganisms involved in black tea processing.

Effect of Fermentation Humidity on Quality of Congou Black Tea.

This study investigated the effect of different fermentation humidities (55%, 65%, 75%, 85% and 95%) on congou black tea quality and bioactivity. Fermentation humidity mainly affected the tea's appearance, aroma and taste quality. The tea fermented at low humidity (75% or below) showed a decrease in tightness, evenness and moistening degree, as well as a heavy grassy and greenish scent, plus a green, astringent and bitter taste. The tea fermented at a high humidity (85% or above) presented a sweet and pure aroma, as well as a mellow taste, plus an increase of sweetness and umami. With increasing fermentation humidity, the tea exhibited a drop in the content of flavones, tea polyphenols, catechins (EGCG, ECG) and theaflavins (TF, TF-3-G), contrasted by a rise in the content of soluble sugars, thearubigins and theabrownins, contributing to the development of a sweet and mellow taste. Additionally, the tea showed a gradual increase in the total amount of volatile compounds and in the content of alcohols, alkanes, alkenes, aldehydes, ketones and acids. Moreover, the tea fermented at a low humidity had stronger antioxidant activity against 2, 2-Diphenyl-1-picrylhydrazyl (DPPH) and a higher inhibiting capability on the activities of α-amylase and α-glucosidase. Overall results indicated the desirable fermentation humidity of congou black tea should be 85% or above.

Effectiveness of mixed tea plant extract gel with chitosan on fibroblast cells after tooth extraction of Wistar rats

ABSTRACTIntroduction: Tooth extraction can cause the damage of hard and soft tissue. When an injury occurs, fibroblast will respond in the wound healing process. Herbal medicines such as green tea and chitosan can be used in wound healing. The polyphenol content in green tea, namely EGCG, has an anti-inflammatory effect, increasing wound healing. In wound healing, chitosan promotes hemostasis and tissue recovery. Based on several previous studies, the use of 1.2% green tea extract was effective for improving wound healing in rat open wound models, chitosan 1% could trigger the proliferation of fibroblasts in the wound healing process after tooth extraction. The aim of this study is to analyze the effect of gel mixture of extract Camellia sinensis 1,2% with chitosan 1% on post-tooth extraction wound of Wistar rats. Methods: This was a randomized post-test only control group design using 40 Wistar rats which were randomly divided into 2 groups. Each group underwent intramuscular anesthesia on the rat's thigh and tooth extraction of mandibular left incisor. The treatment group was applied mixed gel of extract Camellia sinensis 1.2% and 1% chitosan as much as 0.01 ml and the control group was not given any treatment, after that decapitated on days 1, 3, 5, and 7. Result: The mean number of treated fibroblasts was higher than the control group on days 1, 3, 5, 7, respectively, 75.00; 176.00; 349.00 and 427.00 cells. The mean difference in the number of fibroblasts was significant with p-value 0.001 (p&gt;0.05). Conclusions: Mixed extract gel of Camellia sinensis and chitosan increased fibroblasts in wound healing process after tooth extraction of Wistar rats with the highest mean of fibroblast on the 7th day.Keywords: mixed tea leaves,chitosan, fibroblast,wound healing

Monitoring black tea fermentation quality by intelligent sensors: Comparison of image, e-nose and data fusion

To scientifically and objectively monitor the fermentation quality of black tea, a computer vision system (CVS) and electronic nose (e-nose) were employed to analyze the black tea image and odor eigenvalues of Yinghong No. 9 black tea. First, the variation trends of tea polyphenols, volatile substances, image eigenvalues and odor eigenvalues with the extension of fermentation time were analyzed, and the fermentation process was categorized into three stages for classification. Second, principal component analysis (PCA) was employed on the image and odor eigenvalues obtained by CVS and e-nose. Partial least squares discriminant analysis (PLS-DA) was performed on 117 volatile components, and 51 differential volatiles were screened out based on variable importance in projection (VIP ≥1) and one-way analysis of variance (P < 0.05), including geraniol, linalool, nerolidol, and α-ionone. Then, image features and odor features are fused by using a data fusion strategy. Finally, the image, smell and fusion information were combined with random forest (RF), K-nearest neighbor (KNN) and support vector machine (SVM) to establish the classification models of different fermentation stages and to compare them. The results show that the feature-level fusion strategy integrating the SVM was the most efficient approach, with classification accuracy rates of 100% for the training sets and 95.6% for the testing sets. The performance of Support Vector Regression (SVR) prediction models for tea polyphenol content based on feature-level fusion data outperformed data-level models (Rc, RMSEC, Rp and RMSEP of 0.96, 0.48 mg/g, 0.94, 0.6 mg/g).

Effects of Soil Physical and Chemical Properties on the Quality of Nanjing ‘Yuhua’ Tea, a Type of Famous Green Tea

Tea has become one of the most popular drinks worldwide because of its special taste and health benefits. ‘Yuhua’ tea is a famous specialty of Nanjing, China, a green tea produced from Camelia sinensis. It belongs to the green tea category and is one of the top ten most famous teas in China. The physical and chemical properties of soil are very important to the quality of tea. However, at present, there are few instances of research on the soil of the ‘Yuhua’ tea cultivation area. To provide the theoretical basis for improving the final quality of ‘Yuhua’ tea by soil management measures, the effects of the soil’s physical and chemical properties on the contents of important chemical components (free amino acids, tea polyphenols, and caffeine) of ‘Yuhua’ tea in three different cultivation areas in Nanjing were studied. The quality of ‘Yuhua’ tea is greatly influenced by the physical and chemical properties of the soil in which the tea plant grows. The free amino acid content and tea polyphenol content of tea leaves were significantly and negatively correlated with soil bulk density, pH, and water-soluble calcium content, while a significantly positive correlation was noticed between the two criteria and soil effective nitrogen, phosphorus, and potassium content. Additionally, the growth of the tea tree will be hampered and perhaps stressed if the soil’s helpful mineral concentration is too low. The tea tree may produce more caffeine as a result, which could lower the quality of the tea leaves. Therefore, implementing targeted soil management techniques is the key to promoting tea tree growth and improving tea quality. This study laid the foundation for the adoption of corresponding soil management measures to improve the quality of ‘Yuhua’ tea.

Tea Polyphenols Reduce Inflammation of Orbital Fibroblasts in Graves’ Ophthalmopathy via the NF-κB/NLRP3 Pathway

ObjectiveThis study aimed to explore the effects of tea polyphenols (TP) on inflammation of orbital fibroblasts in Graves’ ophthalmopathy (GO) and to provide new ideas for GO treatment.MethodsPrimary orbital fibroblasts were extracted from orbital adipose/connective tissues of patients with and without GO. Real-time quantitative PCR (RT-qPCR) was used to detect the expression of interleukin (IL)-6, IL-1β, and monocyte chemotactic protein (MCP)-1 in non-GO and GO orbital fibroblasts. The CCK-8 assay was used to determine the appropriate concentration of TP for subsequent experiments. RT-qPCR and enzyme-linked immunosorbent assay (ELISA) were performed to investigate the effects of TP on lipopolysaccharide (LPS)-induced production of inflammatory cytokines. Nuclear factor-κB (NF-κB) expression was measured using Western blotting analysis. NOD-like receptor 3 (NLRP3) expression was detected using both Western blotting analysis and immunofluorescence staining.ResultsThe mRNA levels of IL-6, IL-1β, and MCP-1 in GO orbital fibroblasts were significantly higher than those in non-GO cells. TP treatment significantly inhibited LPS-induced production of inflammatory factors, including IL-6, IL-1β, and MCP-1. TP also inhibited the expression levels of NF-κB and NLRP3. Inflammation in the GO orbital fibroblasts was higher than that in non-GO cells. TP inhibited the production of inflammatory cytokines in GO orbital fibroblasts in vitro through the NF-κB/NLRP3 pathway.ConclusionThese findings suggest that TP may have a potential role in GO treatment.

Rapid Detection of Tea Polyphenols in Fresh Tea Leaves Based on Fusion of Visible/Short-Wave and Long-Wave near Infrared Spectroscopy and Its Device Development

Tea polyphenols are considered as an important indicator of tea quality. Rapid detection of tea polyphenol content plays a valuable role for tea breeding and quality inspection during tea production. In this work, a portable rapid non-destructive detection device of tea polyphenols in fresh tea leaves was developed, which integrated the fusion technology of visible/short-wave (400–1050 nm) and long-wave (1000–1650 nm) near-infrared spectroscopy (Vis/NIR). Experimental results indicated that the spectra within the overlapping region (1000–1050 nm) were assembled by applying the spectral data fusing method. Followed by spectral data preprocessing with the Savitzky–Golay smoothing (SG) method, least squares support vector regression (LS–SVR) models were established for detecting the tea polyphenol content of fresh tea leaves. Based on the fused Vis/NIR spectra (dual-band), the correlation coefficient of calibration (RC), root mean square error of calibration (RMSEC), correlation coefficient of prediction (RP), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) reached 0.976, 0.679%, 0.893, 0.897%, and 2.230, respectively, which were better than the visible/short-wave or long-wave near infrared spectral data (single-band). The sensitive spectral wavebands of tea polyphenols extracted using the random frog (RF) algorithm were distributed in 402–448 nm, 555–600 nm, 810–1042 nm, 1056–1103 nm, 1219–1323 nm, 1406–1416 nm, and 1499–1511 nm. This demonstrated that the prediction of tea polyphenol content using fused spectral data combined with the LS–SVR model depended on various functional groups such as auxochromes, chromogenic groups, and hydrogen-containing groups. The proposed device is capable of non-destructive detection of tea polyphenol content in fresh tea leaves, which can provide effective technical support for tea breeding and tea leaf quality control.

Determination of Tibetan tea quality by hyperspectral imaging technology and multivariate analysis

Tibetan tea is a dark tea native to Ya'an, and its taste and quality are closely related to the contents of tea polyphenols (TPs) and free amino acids (FAAs). In this study, TPs and FAAs were determined by chemometrics and the grades of Tibetan tea were distinguished. Then, hyperspectral data were collected, a variety of preprocessing methods were used to preprocess the spectral data, and principal component analysis (PCA) was used for feature dimensionality reduction. Results showed that the combination of the preprocessing method and machine learning (ML) could achieve a higher prediction effect. Savitzky Golay (SG)-Standard Normal Variable (SNV)-PCA-Extratree had the best predictive ability for TPs (Rp2=0.9248, RMSEP=0.4842, and RPD=3.646). For detecting FAAs, SG-Multiplicative Scatter Correction (MSC)-PCA-Extratree had the best predictive ability (Rp2=0.8736, RMSEP=0.159, and RPD=2.813). In addition, tea grade can be determined by SG-MSC-PCA-Support Vector Machine (SVM) with 100% accuracy, recall, and precision. In sum, hyperspectral imaging technology (HSI) can be used as an alternative method for rapid, non-destructive testing of tea quality.

Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves.

Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700-1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.

Rapid and non-destructive determination of tea polyphenols content in Chongzhou new loquat tea lines based on near infrared spectroscopy

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.

Effect of tea polyphenols on the physicochemical, structural and digestive properties of modified high amylose corn starch.

In this study, starch-polyphenol complexes (CES-TPS complexes) were prepared using various ratios (0%, 2%, 4%, 6%, 8%, and 10%, based on starch) of tea polyphenols (TPS) and high amylose corn starch (HACS) pretreated with starch branching enzyme (SBE). It was aimed to determine the effects of TPS on the physicochemical and structural properties and digestibility of the CES-TPS complexes. Scanning electron microscopy and laser particle size analysis showed that the addition of a moderate amount of TPS will reinforce interaction force, while excessive TPS will cause a loose structural morphology, leading to an increase in starch particle size. Thermal property analysis indicated that SBE pre-treatment decreased TO, TP and TC of HACS, and the gelatinization temperature was further reduced after adding TPS. The digestion of CES-TPS complexes was investigated using an Artificial Gut analyzer; the predicted glycemic index of starch samples decreased with the addition of a low concentration of TPS (2-6%), while there was a significant increment in the pGI of starch samples when a high concentration of TPS (8-10%) was added. XRD analysis showed that the relative crystallinity of the CES-TPS complexes further increased to 21.91% and then decreased to 19.38% with the increase of TPS concentration. The ratios of 1047/1022 cm-1 presented the opposite trend to that determined by FT-IR.

ANALISIS POLIFENOL EKSTRAK TEH TUBRUK KEMASAN MENGGUNAKAN SPEKTROFOTOMETRI UV-VIS

The purpose of this research is determine the polyphenol content of brewed tea. Sample were used three sample. Polyphenol compounds of packaged tea were extracted using percolation method with ethanol 70%. Qualitative test were used using FeCl3 reagent and quantitative test polyphenol content were determined using UV-Vis spectrophotometry method with Folin Ciocalteau reagent. Galic acid was used as comparator in this research. The result of qualitative test is all sample of packaged tea containing polyphenol. Polyphenol content sample A, B, and C, with peercolation method were respectively 0,538; 0,554; and 0,470; %b/b.

Characterization of Effects of Different Tea Harvesting Seasons on Quality Components, Color and Sensory Quality of "Yinghong 9" and "Huangyu" Large-Leaf-Variety Black Tea.

Harvesting seasons are crucial for the physicochemical qualities of large-leaf-variety black tea. To investigate the effect of harvesting seasons on physicochemical qualities, the color and sensory characteristics of black tea produced from "Yinghong 9" (Yh) and its mutant "Huangyu" (Hy) leaves were analyzed. The results demonstrated that Hy had better chemical qualities and sensory characteristics, on average, such as a higher content of tea polyphenols, free amino acids, caffeine, galloylated catechins (GaCs) and non-galloylated catechins (NGaCs), while the hue of the tea brew (ΔE*ab and Δb*) increased, which meant that the tea brew was yellower and redder. Moreover, the data showed that the physicochemical qualities of SpHy (Hy processed in spring) were superior to those of SuHy (Hy processed in summer) and AuHy (Hy processed in autumn), and 92.6% of the total variance in PCA score plots effectively explained the separation of the physicochemical qualities of Yh and Hy processed in different harvesting seasons. In summary, Hy processed in spring was superior in its physicochemical qualities. The current results will provide scientific guidance for the production of high-quality large-leaf-variety black tea in South China.