Comparative transcriptomic analysis unveils the deep phylogeny and secondary metabolite evolution of 116 Camellia plants.

Camellia plants include more than 200 species of great diversity and immense economic, ornamental, and cultural values. We sequenced the transcriptomes of 116 Camellia plants from almost all sections of the genus Camellia. We constructed a pan-transcriptome of Camellia plants with 89 394 gene families and then resolved the phylogeny of genus Camellia based on 405 high-quality low-copy core genes. Most of the inferred relationships are well supported by multiple nuclear gene trees and morphological traits. We provide strong evidence that Camellia plants shared a recent whole genome duplication event, followed by large expansions of transcription factor families associated with stress resistance and secondary metabolism. Secondary metabolites, particularly those associated with tea quality such as catechins and caffeine, were preferentially heavily accumulated in the Camellia plants from section Thea. We thoroughly examined the expression patterns of hundreds of genes associated with tea quality, and found that some of them exhibited significantly high expression and correlations with secondary metabolite accumulations in Thea species. We also released a web-accessible database for efficient retrieval of Camellia transcriptomes. The reported transcriptome sequences and obtained novel findings will facilitate the efficient conservation and utilization of Camellia germplasm towards a breeding program for cultivated tea, camellia, and oil-tea plants.

Research on Variable Parameter Drilling Method of Ti-CFRP-Ti Laminated Stacks Based on Real-Time Sensing of Drilling Axial Force.

Ti-CFRP-Ti laminated stacks have been widely used in aviation, aerospace, shipbuilding and other industries, owing to its excellent physical and electrochemical properties. However, chip blockages occur easily when drilling into Ti-CFRP-Ti laminated stacks, resulting in a rapid rise of drilling temperature and an increase of axial drilling force, which may lead to the intensification of tool wear and a decline of drilling quality. Cutting force signals can effectively reflect the drilling process and tool condition, however, the traditional plate dynamometer is typically difficult in realizing the follow-up online measurement. Therefore, an intelligent tool holder system for real-time sensing of the cutting force is developed and constructed in this paper, and the variable parameter drilling method of Ti-CFRP-Ti laminated stacks is studied on this basis. Firstly, an intelligent tool holder system with high flexibility and adaptability is designed; Secondly, a cutting force signal processing method based on compressed sensing (CS) theory is proposed to solve the problem of high-frequency signal transmission; Lastly, the drilling experiment of Ti-CFRP-Ti laminated stacks is carried out based on the intelligent tool holder system, and the drilling parameters are optimized using a compromise programming approach and analytic hierarchy process (AHP). The comparison of results show that the optimized drilling parameters can effectively reduce the hole wall surface roughness and improve the drilling efficiency while ensuring a small axial force.

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.

Draft genome sequence of Camellia sinensis var. sinensis provides insights into the evolution of the tea genome and tea quality.

Tea, one of the world's most important beverage crops, provides numerous secondary metabolites that account for its rich taste and health benefits. Here we present a high-quality sequence of the genome of tea, Camellia sinensis var. sinensis (CSS), using both Illumina and PacBio sequencing technologies. At least 64% of the 3.1-Gb genome assembly consists of repetitive sequences, and the rest yields 33,932 high-confidence predictions of encoded proteins. Divergence between two major lineages, CSS and Camellia sinensis var. assamica (CSA), is calculated to ∼0.38 to 1.54 million years ago (Mya). Analysis of genic collinearity reveals that the tea genome is the product of two rounds of whole-genome duplications (WGDs) that occurred ∼30 to 40 and ∼90 to 100 Mya. We provide evidence that these WGD events, and subsequent paralogous duplications, had major impacts on the copy numbers of secondary metabolite genes, particularly genes critical to producing three key quality compounds: catechins, theanine, and caffeine. Analyses of transcriptome and phytochemistry data show that amplification and transcriptional divergence of genes encoding a large acyltransferase family and leucoanthocyanidin reductases are associated with the characteristic young leaf accumulation of monomeric galloylated catechins in tea, while functional divergence of a single member of the glutamine synthetase gene family yielded theanine synthetase. This genome sequence will facilitate understanding of tea genome evolution and tea metabolite pathways, and will promote germplasm utilization for breeding improved tea varieties.

Evaluation of Dianhong black tea quality using near-infrared hyperspectral imaging technology.

BACKGROUND: Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms. RESULTS: Two matrix statistical algorithms encompassing a gray-level co-occurrence matrix (GLCM) and a gradient co-occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near-infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%. CONCLUSION: This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry.

Cognitive spectroscopy for evaluating Chinese black tea grades (Camellia sinensis): near-infrared spectroscopy and evolutionary algorithms.

BACKGROUND: Grading represents an essential criterion for the quality assurance of black tea. The main objectives of the study were to develop a highly robust model for Chinese black tea of seven grades based on cognitive spectroscopy. RESULTS: Cognitive spectroscopy was proposed to combine near-infrared spectroscopy (NIRS) with machine learning and evolutionary algorithms, selected feature information from complex spectral data and show the best results without human intervention. The NIRS measuring system was used to obtain the spectra of Chinese black tea samples of seven grades. The spectra acquired were preprocessed by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and minimum/maximum normalization (MIN/MAX), and the optimal pretreating method was implemented using principal component analysis combined with linear discriminant analysis algorithm. Three feature selection evolutionary algorithms, which were a genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), were compared to search the best preprocessed characteristic wavelengths. Cognitive models of Chinese black tea ranks were constructed using extreme learning machine (ELM), K-nearest neighbor (KNN) and support vector machine (SVM) methods based on the selected characteristic variables. Experimental results revealed that the PSO-SVM model showed the best predictive performance with the correlation coefficients of prediction set (Rp ) of 0.9838, the root mean square error of prediction (RMSEP) of 0.0246, and the correct discriminant rate (CDR) of 98.70%. The extracted feature wavelengths were only occupying 0.18% of the origin. CONCLUSION: The overall results demonstrated that cognitive spectroscopy could be utilized as a rapid strategy to identify Chinese black tea grades. © 2020 Society of Chemical Industry.

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.

Significantly increased amino acid accumulation in a novel albino branch of the tea plant (Camellia sinensis).

MAIN CONCLUSION: A normal tea plant with one albino branch was discovered. RNA sequencing, albinism phenotype and ultrastructural observations provided a valuable understanding of the albino mechanism in tea plants. Tea plants with a specific color (white or yellow) have been studied extensively. A normal tea plant (Camellia sinensis cv. quntizhong) with one albino branch was discovered in a local tea plantation in Huangshan, Anhui, China. The pure albino leaves on this special branch had accumulated a fairly high content of amino acids, especially theanine (45.31 mg/g DW), and had a low concentration of polyphenols and an extremely low chlorophyll (Chl) content compared with control leaves. Ultrastructural observation of an albino leaf revealed no chloroplasts, whereas it was viable in the control leaf. RNA sequencing and differentially expressed gene (DEG) analysis were performed on the albino leaves and on control leaves from a normal green branch. The related genes involved in theanine and polyphenol biosynthesis were also investigated in this study. DEG expression patterns in Chl biosynthesis or degradation, carotenoid biosynthesis or degradation, chloroplast development, and biosynthesis were influenced in the albino leaves. Chloroplast deletion in albino leaves had probably destroyed the balance of carbon and nitrogen metabolism, leading to a high accumulation of free amino acids and a low concentration of polyphenols in the albino leaves. The obtained results can provide insight into the mechanism underlying this special albino branch phenotype, and are a valuable contribution toward understanding the albino mechanism in tea plants.

Tea Plant Information Archive: a comprehensive genomics and bioinformatics platform for tea plant.

Tea is the world's widely consumed nonalcohol beverage with essential economic and health benefits. Confronted with the increasing large-scale omics-data set particularly the genome sequence released in tea plant, the construction of a comprehensive knowledgebase is urgently needed to facilitate the utilization of these data sets towards molecular breeding. We hereby present the first integrative and specially designed web-accessible database, Tea Plant Information Archive (TPIA; http://tpia.teaplant.org). The current release of TPIA employs the comprehensively annotated tea plant genome as framework and incorporates with abundant well-organized transcriptomes, gene expressions (across species, tissues and stresses), orthologs and characteristic metabolites determining tea quality. It also hosts massive transcription factors, polymorphic simple sequence repeats, single nucleotide polymorphisms, correlations, manually curated functional genes and globally collected germplasm information. A variety of versatile analytic tools (e.g. JBrowse, blast, enrichment analysis, etc.) are established helping users to perform further comparative, evolutionary and functional analysis. We show a case application of TPIA that provides novel and interesting insights into the phytochemical content variation of section Thea of genus Camellia under a well-resolved phylogenetic framework. The constructed knowledgebase of tea plant will serve as a central gateway for global tea community to better understand the tea plant biology that largely benefits the whole tea industry.

Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems.

BACKGROUND: The instrumental evaluation of tea quality using digital sensors instead of human panel tests has attracted much attention globally. However, individual sensors do not meet the requirements of discriminant accuracy as a result of incomprehensive sensor information. Considering the major factors in the sensory evaluation of tea, the study integrated multisensor information, including spectral, image and olfaction feature information. RESULTS: To investigate spectral and image information obtained from hyperspectral spectrometers of different bands, principal components analysis was used for dimension reduction and different types of supervised learning algorithms (linear discriminant analysis, K-nearest neighbour and support vector machine) were selected for comparison. Spectral feature information in the near infrared region and image feature information in the visible-near infrared/near infrared region achieved greater accuracy for classification. The results indicated that a support vector machine outperformed other methods with respect to multisensor data fusion, which improved the accuracy of evaluating green tea quality compared to using individual sensor data. The overall accuracy of the calibration set increased from 75% using optimal single sensor information to 92% using multisensor information, and the overall accuracy of the prediction set increased from 78% to 92%. CONCLUSION: Overall, it can be concluded that multisensory data accurately identify six grades of tea. © 2018 Society of Chemical Industry.

Identification of geographical origin of Keemun black tea based on its volatile composition coupled with multivariate statistical analyses.

BACKGROUND: Keemun black tea (KBT) is one of the most popular tea beverages in China as a result of its unique flavor and potential health benefits. The geographical origin of KBT influences its quality and price. The present study aimed to apply a head-space solid phase microextraction approach and gas chromatography-mass spectrometry combined with chemometric analysis to profile the volatile compounds of KBT collected from five production areas. RESULTS: Thirty-one peaks were detected in 61 KBT samples. Hierarchical cluster analysis, principal component analysis (PCA), k-nearest neighbor (k-NN) and stepwise linear discriminant analysis (SLDA) were employed to visualize the volatile fractions. The results of unsupervised statistical tools were compared using a test for similarities and distinctions, which showed that different sources may be associated. A satisfying combination of average recognition (91.7%) and cross-validation prediction abilities (84.6%) was obtained for the PCA-k-NN. Among all of the statistical tools, SLDA provided promising results, with 100% recognition and 96.4% prediction ability. CONCLUSION: The results obtained in the present study indicate that the volatile compounds can be used as indicators to identify the geographical origin of KBT. © 2019 Society of Chemical Industry.

Rapid prediction of chlorophylls and carotenoids content in tea leaves under different levels of nitrogen application based on hyperspectral imaging.

BACKGROUND: Photosynthetic pigments perform critical physiological functions in tea plants. Their content is an essential indicator of photosynthetic efficiency and nutritional status. The present study aimed to predict chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (total Chl), and carotenoid (Car) content in tea leaves under different levels of nitrogen treatment using hyperspectral imaging (HSI) in combination with variable selection algorithms. RESULTS: A total of 150 samples were collected and scanned using the HSI system. The mean spectrum in the region of interest (ROI) was extracted, and the pigment content was measured by traditional chemical methods. Five and seven optimal wavelengths (OWs) were selected using the regression coefficients (RCs) of partial least squares regression (PLSR) and the second-derivative (2-Der), respectively. The optimal 2-Der-PLSR models for Chl a, Chl b, total Chl, and Car performed remarkably well based on seven OWs with correlation coefficients of prediction (RP ) of 0.9337, 0.9322, 0.9333 and 0.9036, root mean square errors in prediction (RMSEP) of 0.1100, 0.0511, 0.1620, and 0.0300 mg g-1 , respectively. CONCLUSION: The results of this study revealed that HSI combined with variable selection method can be employed as a rapid and accurate method for predicting the content of pigments in tea plants. © 2018 Society of Chemical Industry.

Evaluation of matcha tea quality index using portable NIR spectroscopy coupled with chemometric algorithms.

BACKGROUND: The study reports a portable near infrared (NIR) spectroscopy system coupled with chemometric algorithms for prediction of tea polyphenols and amino acids in order to index matcha tea quality. RESULTS: Spectral data were preprocessed by standard normal variate (SNV), mean center (MC) and first-order derivative (1st D) tests. The data were then subjected to full spectral partial least squares (PLS) and four variable selection algorithms, such as random frog partial least square (RF-PLS), synergy interval partial least square (Si-PLS), genetic algorithm-partial least square (GA-PLS) and competitive adaptive reweighted sampling partial least square (CARS-PLS). RF-PLS was established and identified as the optimum model based on the values of the correlation coefficients of prediction (RP ), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD), which were 0.8625, 0.82% and 2.13, and 0.9662, 0.14% and 3.83, respectively, for tea polyphenols and amino acids. The content range of tea polyphenols and amino acids in matcha tea samples was 8.51-14.58% and 2.10-3.75%, respectively. The quality of matcha tea was successfully classified with an accuracy rate of 83.33% as qualified, unqualified and excellent grade. CONCLUSION: The proposed method can be used as a rapid, accurate and non-destructive platform to classify various matcha tea samples based on the ratio of tea polyphenols to amino acids. © 2019 Society of Chemical Industry.

Using stable isotope signatures to delineate the geographic point-of-origin of Keemun black tea.

BACKGROUND: Confirmation of food labeling that claims production in a small geographic region is critical to traceability, quality control and brand protection. In the current study, isotope ratio mass spectrometry (IRMS) was used to generate profiles of δ13 C and δ15 N to determine if the stable isotope signatures of Keemun black tea differ within the three counties that claim production. Other factors (cultivar type, leaf maturity and manufacturing process) were considered for their potential effects. RESULTS: Both cultivar type and leaf maturity have remarkable impact on the δ15 N values of tea leaves, and that the cultivar influenced the δ13 C values. Keemun black tea from Qimen county could be easily discriminated from samples from Dongzhi and Guichi counties based on δ15 N signatures. The k-NN model was cross-validated with an accuracy of 91.6%. Environmental factors and/or genotype seem to be the major reasons for δ15 N differences in Keemun black tea from the selected regions. CONCLUSION: This article provides a potential effective method to delineate the geographic point-of-origin of Keemun black tea based on δ15 N signatures. © 2018 Society of Chemical Industry.

The impacts of brewing in glass tumblers and thermos vacuum mugs on the aromas of green tea (Camellia sinensis).

This study investigated the effect of brewing apparatus on the aromatic feature of tea infusion. Huangshan Maofeng tea infusion was brewed under glass tumblers (GT) or thermos vacuum mugs (TVM) for up to 180 min. Tea infusion sensory attributes were evaluated using quantitative descriptive analysis and the composition of volatiles were analyzed using headspace solid phase microextraction coupled with gas chromatography-mass spectrometry. Results showed that GT tea infusion at each brewing duration possessed stronger 'Pure', 'Fresh' and 'Grassy' attributes than TVM tea infusion, whereas TVM tea infusion showed a higher intensity on 'Steamed' aroma. A total of 74 volatiles were detected in tea infusion, and aldehydes and alcohols appeared to be the major volatiles. Total aldehydes concentration percentage decreased in tea infusion with brewing process, whereas an increase on total alcohol percentage was found. Principal component analysis indicated that brewing duration and apparatus played vital roles in altering the volatile composition in tea infusion, whereas orthogonal partial least squares discriminant analysis (OPLS-DA) revealed that GT tea infusion samples were separated from TVM tea infusion samples. OPLS-DA also screened 20 volatiles that significantly contributed to the differentiation of GT and TVM tea infusion.

Recommended storage temperature for green tea based on sensory quality.

This study aimed to evaluate the effect of storage temperature on the alteration of the sensory quality of tea. Huangshan Maofeng tea was stored at - 80 °C, - 20 °C, 4 °C, or room temperature for up to 150 days. The physicochemical parameters, taste-related components, appearance color, volatile compounds and sensory quality of tea were analyzed and compared. Results showed that storing tea at - 80 °C and - 20 °C effectively preserved the physicochemical parameters, taste-related compounds and appearance color in tea. Multivariate statistical analysis (PCA and OPLS-DA) indicated that tea stored at - 80 °C exhibited a similar volatiles composition as fresh tea based on gas chromatography-mass spectrometry, whereas the composition of volatiles was significantly altered in tea stored at 4 °C after 100 days of storage. Sensory evaluation illustrated that tea stored at - 80 °C and - 20 °C remained the freshness regarding leaves appearance and tea infusion color, taste and aroma, whereas an obvious decrease on the tea freshness was found in tea stored at 4 °C and room temperature. These findings indicated that storage temperature played a vital role in altering the aromatic and sensory quality of Huangshan Maofeng tea and the recommended tea storage temperature was - 80 °C or - 20 °C.

Enantioselective Bromocyclization of Tryptamines Induced by Chiral Co(III)-Complex-Templated Brønsted Acids under an Air Atmosphere.

The chiral Co(III)-complex-templated Brønsted acids were found to be efficient bifunctional phase-transfer catalysts for the highly enantioselective bromocyclization of protected tryptamines with readily available N-bromosuccinimide (NBS) under an air atmosphere. The 3-bromohexahydropyrrolo[2,3- b]indoles, which are key building blocks of cyclotryptamine alkaloids, were thus obtained in up to 95% yield and 93.5:6.5 er.

Discrimination of nitrogen fertilizer levels of tea plant (Camellia sinensis) based on hyperspectral imaging.

BACKGROUND: Nitrogen (N) fertilizer plays an important role in tea plantation management, with significant impacts on the photosynthetic capacity, productivity and nutrition status of tea plants. The present study aimed to establish a method for the discrimination of N fertilizer levels using hyperspectral imaging technique. RESULTS: Spectral data were extracted from the region of interest, followed by the first derivative to reduce background noise. Five optimal wavelengths were selected by principal component analysis. Texture features were extracted from the images at optimal wavelengths by gray-level gradient co-occurrence matrix. Support vector machine (SVM) and extreme learning machine were used to build classification models based on spectral data, optimal wavelengths, texture features and data fusion, respectively. The SVM model using fused data gave the best performance with highest correct classification rate of 100% for prediction set. CONCLUSION: The overall results indicated that visible and near-infrared hyperspectral imaging combined with SVM were effective in discriminating N fertilizer levels of tea plants. © 2018 Society of Chemical Industry.

Multiple responses optimization of instant dark tea production by submerged fermentation using response surface methodology.

In this study, submerged fermentation mode for preparing instant dark tea production was developed through utilizing industrial low grade green tea as raw material and Aspergillus niger as fermentation microbe starter. The fermentation parameters (inoculum size, liquid-solid ratio and rotation speed) were optimized by using Box-Behnken design and response surface methodology (RSM) with desirability function, the theabrownins content, redness and turbidity value as responses. The optimal conditions were set as follow: inoculum size of 5.3% (v/v), liquid-solid ratio of 27.78 mL/g, and rotation speed of 182 r/min. The optimized conditions model showed a good correlation between the predicted and experimental values. Further, the optimum product of instant dark was achieved in a 3-L laboratory fermenter, and the main parameters of product were theabrownins content of 140.92 g/kg and redness value of 40.78 and turbidity of 90.98 NTU. Sensory evaluation showed that the instant dark tea infusion approached mellow mouthfeel, an aroma of mint and a good overall acceptance.