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Fermentation is a key black tea processing step and makes an important contribution to quality formation. Current approaches to fermentation monitoring are costly or laboratory-based. Here, we first evaluated the potential of at-line computer vision for detecting fermentation quality in a tea factory. A self-built industrial camera was used to collect tea samples at various fermentation durations. The correlations of color variables that were extracted from the images with key quality indicators in the tea samples were verified. Subsequently, partial least-squares regression models based on the color variables showed high prediction accuracy with residual prediction deviation values of 4.13, 3.53, and 3.39 for catechins, theaflavins and chlorophylls, respectively. Finally, the spatial and temporal distributions of indicators during fermentation were mapped to visualize the fermentation quality. This study realized low-cost, at-line and real-time detection for black tea fermentation, which provides technical support for the industrial and intelligent production of black tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The taste of tea is one of the key indicators in the evaluation of its quality and is a key factor in its grading and market pricing. To objectively and digitally evaluate the taste quality of tea leaves, miniature near-infrared (NIR) spectroscopy and electronic tongue (ET) sensors are considered effective sensor signals for the characterization of the taste quality of tea leaves. This study used micro-NIR spectroscopy and ET sensors in combination with data fusion strategies and chemometric tools for the taste quality assessment and prediction of multiple grades of black tea. Using NIR features and ET sensor signals as fused information, the data optimization based on grey wolf optimization, ant colony optimization (ACO), particle swarm optimization, and non-dominated sorting genetic algorithm II were employed as modeling features, combined with support vector machine (SVM), extreme learning machine and K -nearest neighbor algorithm to build the classification models. The results obtained showed that the ACO-SVM model had the highest classification accuracy with a discriminant rate of 93.56%. The overall results reveal that it is feasible to qualitatively distinguish black tea grades and categories by NIR spectroscopy and ET techniques.

Withering is one of the most critical steps in the processing of black tea. The degree of withering affects the aroma quality of the finished tea. In this study, we used a pH indicator-based colorimetric sensor array in combination with hyperspectral imaging to intelligently evaluate the withering degree. After analyzing the difference between images taken before and after the reaction of pH indicators with withered leaves, six pH indicators were selected to build a sensor array. Then, the hyperspectral image of each pH indicator was obtained at wavelengths between 400 and 1000 nm. Nonlinear support vector machine (SVM) and least-squares (LS) SVM models were established to determine the degree of withering. Results revealed that the spectral information from single pH indicator failed to accurately evaluate the withering degree. The LS-SVM model achieved satisfactory discriminant results with the low-level data fusion of six pH indicators followed by principal component analysis for dimensionality reduction. The optimal model yielded accuracies of 93.75% and 90.00% for the calibration and prediction sets, respectively. The results indicated that colorimetric sensor array in combination with hyperspectral imaging can effectively determine the withering degree, thus providing a novel method for the intelligent processing of food and tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Rapid and low-cost testing tools provide new methods for the evaluation of tea quality. In this study, a micro near-infrared (NIR) spectrometer was used for the qualitative and quantitative evaluation of tea. A total of 360 tea samples consisting of black, green, yellow, and oolong tea were collected from different countries. Chemometrics including linear partial least squares (PLS) regression, PLS discriminant analysis, and nonlinear radial basis function-support vector machine (RBF-SVM) were used. The RBF-SVM model achieved optimal discriminant performance for tea types with a correct classification rate of 98.33%. Wavelength selection of iteratively variable subset optimization (IVSO) exhibited considerable advantages in improving the predictive performance of catechin, caffeine, and theanine models. The IVSO-PLS regression models achieved satisfactory results for catechins and caffeine prediction, with Rp over 0.9, and RPD over 2.5. Thus, the study provided a portable and low-cost method for in-situ assessing tea quality., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., (© 2020 Society of Chemical Industry.)

Green tea adulterated with sugar and glutinous rice flour has an increased sensitivity to water, which affects the safety of the tea. A total of 475 samples of pure tea, sugar-adulterated tea, and glutinous-rice-flour-adulterated tea were prepared and scanned using micro near infrared spectroscopy (NIRS). The collected NIRS data were qualitatively and quantitatively detected by a multi-layer algorithm model. Principal component analysis indicated that the three sample groups had an obvious separation trend. The discriminate rate of the optimal qualitative model, namely support vector machine, was 97.47% for the prediction set. A total of three wavelength selection methods were used to improve the performances of partial least squares regression and support vector machine regression (SVR) models. The nonlinear SVR models based on characteristic wavelengths selected by iteratively retaining informative variables algorithm provided satisfactory results for the identification of sugar and glutinous rice flour adulteration. The correlation coefficients for prediction (Rp) were >0.94, and the residual prediction deviation were >3. The results indicated that smartphone-based micro NIRS can be effectively used to qualitatively and quantitatively analyze adulterants in green tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Tea quality is generally assessed through panel sensory evaluation, which requires elaborate sample preparation steps. Here, a novel and low-cost evaluation method of using smartphone imaging coupled with micro-near-infrared (NIR) spectrometer based on digital light processing is proposed to classify the quality grades of Keemun black tea. RGB color information was obtained by Image J software, eight texture characteristics, including scheme, contrast, dissimilarity, entropy, correlation, second moment and variance, and homogeneity were obtained by ENVI software based on co - occurrence method from smartphone images, and spectral data were preprocessed with standard normal variate. A principal component analysis (PCA)-support vector machine (SVM) model was established to analyze the color, texture, and spectral data. Low-level and middle-level fusion strategies were introduced for analyzing the fusion data. The results indicated that the accuracy of the SVM model on mid-level data fusion (100.00%, 94.29% for calibration set and prediction set, respectively) was higher than that obtained for separate color (97.14%, 88.57%), texture (84.29%, 60%), spectrum (74.29%, 68.57%) evaluation, or low-level data fusion (88.57%, 82.86%). The best SVM model yielded satisfactory performance with 94.29% accuracy for the prediction sets. These results suggested that smartphone imaging coupled with micro-NIR spectroscopy is an effective and low-cost tool for evaluating tea quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Caffeine and catechin are two main components of instant green tea, and are essential components of tea quality. This paper mainly focuses on the feasibility of rapidly determining instant green tea components by using a portable near infrared (NIR) spectrometer. The two main components (caffeine and catechin) were studied. In addition, the instrument performance levels of portable and benchtop NIR spectrometers were studied and compared. Quantitative models developed using portable and benchtop spectrometers for measuring caffeine, total catechins, and four individual catechins were established and compared. After preprocessing using standard normal variate (SNV), the Rp values of the caffeine, total catechins, (-)-epigallocatechin, (-)-epigallocatechin 3-gallate, (-)-epicatechin, and (-)-epicatechin gallate in the partial least squares models for a portable NIR spectrometer were 0.974, 0.962, 0.669, 0.945, 0.942 and 0.905, respectively. For a benchtop NIR spectrometer, Rp values were 0.993, 0.958, 0.883, 0.955, 0.966 and 0.936, respectively. Passing-Bablok regression method results indicated no significant differences between the two instruments. A genetic algorithm (GA) and the successive projections algorithm (SPA) were used to screen the wavelength of the NIR spectrum and establish the model. The GA obtained more robust modeling results. This study concludes that the developed portable spectroscopy system combined with appropriate variable selection methods can be effectively used for rapid determination of caffeine, total catechins, and four individual catechins in instant green tea., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The evaluation of tea quality tended to be subjective and empirical by human panel tests currently. A convenient analytical approach without human involvement was developed for the quality assessment of tea with great significance. In this study, near-infrared hyperspectral imaging (HSI) combined with multiple decision tree methods was utilized as an objective analysis tool for delineating black tea quality and rank. Data fusion that integrated texture features based on gray-level co-occurrence matrix (GLCM) and short-wave near-infrared spectral features were as the target characteristic information for modeling. Three different types of supervised decision tree algorithms (fine tree, medium tree, and coarse tree) were proposed for the comparison of the modeling effect. The results indicated that the performance of models was enhanced by the multiple perception feature fusion. The fine tree model based on data fusion obtained the best predictive performance, and the correct classification rate (CCR) of evaluating black tea quality was 93.13% in the prediction process. This work demonstrated that HSI coupled with intelligence algorithms as a rapid and effective strategy could be successfully applied to accurately identify the rank quality of black tea., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

From the perspective of combating fraud issues and examining keemun black tea properties, there was a contemporary urgent demand for a keemun black tea rankings identification system. Current rapid evaluation systems had been mainly developed for green tea grade evaluation, but there was space for improvement to establish a highly robust model. The present study proposed cognitive spectroscopy that combined near infrared spectroscopy (NIRS) with multivariate calibration and feature variable selection methods. We defined "cognitive spectroscopy" as a protocol that selects characteristic information from complex spectral data and showed optimal results without human intervention. 700 samples representing keemun black tea from seven quality levels were scanned applying an NIR sensor. To differentiate which wavelength variables of the acquired NIRS data carry key and feature information regarding keemun black tea grades, there were four different variables screening approaches, namely genetic algorithm (GA), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and shuffled frog leaping algorithm (SFLA), were compared in this study. Cognitive models were developed using least squares support vector machine (LSSVM), back propagation neural network (BPNN) and random forest (RF) methods combined with the optimized characteristic variables from the above variables selection algorithms for the identification of keemun black tea rank quality. Experimental results showed that all cognitive models utilizing the SFLA approach achieved steady predictive results based on eight latent variables and selected thirteen characteristic wavelength variables. The CARS-LSSVM model with the best predictive performance was proposed based on selecting ten characteristic latent variables, and the best performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0413, the correlation coefficients of prediction set (R p ) was 0.9884, and the correct discriminant rate (CDR) was 99.01% in the validation process. This study demonstrated that cognitive spectroscopy represented a proper strategy for the highly identification of quality rankings of keemun black tea., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The tenderness of the fresh tea leaves can affect the quality of tea products. It is important to develop a mechanized, accurate way to evaluate the quality of fresh leaves that avoids the uncertainty of a subjective evaluation. Herein, an in-situ, ultra-rapid Raman microscopy strategy to quantify carotenoids in tea leaves was established. The Raman microscopy of carotenoids distribution in leaves from new branches of 22 representative tea varieties showed that the average carotenoid signals increased from a low level in the bud to a high level in the fourth leaf, which represent different developmental stages. The concentration of carotenoids in the bud to fourth leaf, which were from 69.1 ng mg -1 to 199.5 ng mg -1 , respectively. These results demonstrate that Raman imaging can serve as an in-situ, non-destructive and ultra-rapid technology for determining the tenderness of fresh tea leaves and be used in quality control for tea processing., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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 (1 st 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 (R P ), 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., (© 2019 Society of Chemical Industry.)

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., (© 2019 Society of Chemical Industry.)

A symposium entitled "Chemistry, Flavor, and Health Effects of Tea" was held at the 256th American Chemical Society (ACS) Meeting in August 2018 in Boston, MA, U.S.A., and was sponsored by the ACS Division of Agricultural and Food Chemistry and the International Joint Laboratory on Tea Chemistry and Health Effects, Anhui Agricultural University, China. The purpose of the symposium was to bring together the leading tea researchers throughout the world to discuss the current state of knowledge as well as research needs with respect to chemistry and health beneficial properties of tea. Speakers from North America, Europe, and Asia delivered a total of 35 oral presentations. The presentations covered such diverse topics as polyphenol chemistry and flavor chemistry of tea, metabolomics application to identify the changes of phytochemical composition during processing, and health beneficial effects of drinking tea. This paper is intended to provide a brief summary of the presentations.

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., (© 2018 Society of Chemical Industry.)

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., Competing Interests: The authors declare no conflict of interest., (Copyright © 2018 the Author(s). Published by PNAS.)

Background: Theabrownins (TB) are bioactive components that are usually extracted from Chinese dark tea, in which they are present at low concentrations. The present study aimed to produce an instant dark tea high in theabrownins via submerged fermentation by the fungus Aspergillus niger. Three fermentation parameters that affect theabrownins content (i.e. inoculum size, liquid-solid ratio and rotation speed) were optimized using response surface methodology., Result: Optimum fermentation conditions were modeled to be an inoculum of 5.40% (v/v), a liquid-solid ratio of 27.45 mL g -1 and a rotation speed of 184 rpm and were predicted to yield 292.99 g kg -1 TB. Under these experimentally conditions, the TB content of the instant dark tea was 291.93 g kg -1 . The antioxidant capacity and α-glucosidase and pancreatic lipase inhibitory activities of the high-TB instant black tea were higher than four other typical instant dark tea products., Conclusion: The results of the present study show that careful management of culture conditions can produce a dark tea high in theabrownins. Furthermore, high-theabrownins instant dark tea could serve as a source of bioactive products and be used in functional foods as an ingredient imparting antioxidant properties and the ability to inhibit pancreatic lipase and α-glucosidase. © 2017 Society of Chemical Industry., (© 2017 Society of Chemical Industry.)

Instrumental test of black tea samples instead of human panel test is attracting massive attention recently. This study focused on an investigation of the feasibility for estimation of the color sensory quality of black tea samples using the VIS-NIR spectroscopy technique, comparing the performances of models based on the spectra and color information. In model calibration, the variables were first selected by genetic algorithm (GA); then the nonlinear back propagation-artificial neural network (BPANN) models were established based on the optimal variables. In comparison with the other models, GA-BPANN models from spectra data information showed the best performance, with the correlation coefficient of 0.8935, and the root mean square error of 0.392 in the prediction set. In addition, models based on the spectra information provided better performance than that based on the color parameters. Therefore, the VIS-NIR spectroscopy technique is a promising tool for rapid and accurate evaluation of the sensory quality of black tea samples., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Tea waste is the residue that remains after tea leaves have been extracted by hot water to obtain water-soluble components. The waste contains a re-usable energy substrate and nutrients which may pollute the environment if they are not dealt with appropriately. Other agricultural wastes have been widely studied as substrates for cultivating mushrooms. In the present study, we cultivated oyster mushroom using tea waste as substrate. To study the feasibility of re-using it, tea waste was added to the substrate at different ratios in different experimental groups. Three mushroom strains (39, 71 and YOU) were compared and evaluated. Mycelia growth rate, yield, biological efficiency and growth duration were measured., Results: Substrates with different tea waste ratios showed different growth and yield performance. The substrate containing 40-60% of tea waste resulted in the highest yield., Conclusion: Tea waste could be used as an effective and economic substrate for oyster mushroom cultivation. This study also provided a useful way of dealing with massive amounts of tea waste., (© 2015 Society of Chemical Industry.)

The current study investigated the effects of green tea polyphenols (GTPs) on lipid metabolism and its mechanisms using broiler chickens (Gallus gallus domesticus). A total of 36 male chickens (35 days old) had been subjected to an oral administration of GTPs at a dosage of 0, 50 (low), and 100 (high) mg/kg of body weight for 20 days. Our results showed that GTPs significantly decreased the abdominal and subcutaneous fat masses of broilers and reduced the serum triglyceride, total cholesterol, and low-density lipoprotein cholesterol levels compared to those of the control. Furthermore, the expression levels for lipid anabolism genes were significantly downregulated, while the expression levels of fat transportation and catabolism-related genes, carnitine palmitoyl transferase I (CPT-I), acyl-CoA oxidase 1 (ACOX1), and peroxisome proliferator-activated receptor-α (PPARα) in liver, adipose triglyceride lipase (ATGL) in abdominal fat, and lipoprotein lipase (LPL) in skeletal muscles, were notably upregulated. Our data have revealed that GTPs alleviate obesity and serum lipid levels in broiler chickens by suppressing fatty acid synthesis and stimulating lipolysis.

A novel dynamic ultrasound-assisted extraction (DUAE) device has been constructed for extraction of five catechins and caffeine in solid sample. The accurate, simple, reproducible and sensitive method for the determination of five catechins and caffeine has been developed and validated. A comparison has been made of the efficiencies by employing DUAE and conventionally static ultrasound-assisted extraction (UAE). The proposed method can not only improve extraction efficiency but also decrease time and solvent consumption. The commercial Chinese tea samples, involving green and black teas, are rapidly analyzed by the proposed DUAE method. The linearity, range, selectivity, precision, accuracy, and robustness also showed acceptable values.

The appearance of tea leaves is the most intuitive factor in assessing their quality and is an important indicator of the price. The study utilizes machine vision and hyperspectral image technology to develop a rapid method for assessing the appearance quality of seven grades of Dianhong Congou black tea samples based on shape, color, texture, and feature fusion. Using the shape metrics of length, width, perimeter, narrowness, area, and rectangularity, 88 texture values, and nine color values as features, the effectiveness of intelligent recognition algorithms such as support vector machine, random forest, and least squares support vector machine (LSSVM) in classifying single features (shape, color, and texture) and fused data models of the categories of Dianhong tea samples was explored. The results showed that the accuracy of the feature fusion-based appearance quality evaluation models was all higher than the modeling results of the corresponding single features, and the accuracy of the LSSVM model based on the fused data was 100%. This study showed that feature data fusion reflects the external information of tea leaves more comprehensively and that intelligent classification algorithms combined with image analysis techniques were an effective strategy for the identification of regional black tea. [ABSTRACT FROM AUTHOR]

Geraniol is a potent tea odorant and exists mainly as geranyl glycoside in Camellia sinensis. Understanding the mechanisms of geraniol biosynthesis at molecular levels in tea plants is of great importance for practical improvement of tea aroma. In this study, geraniol and its glycosides from tea plants were examined using liquid chromatography coupled with mass spectrometry. Two candidate geraniol synthase (GES) genes (CsTPS) and two Nudix hydrolase genes (CsNUDX1-cyto and CsNUDX1-chlo) from the tea genome were functionally investigated through gene transcription manipulation and gene chemical product analyses. Our data showed that in tea leaves, levels of geranyl β -primeveroside were dramatically higher than those of geranyl β -glucoside, while free geraniol was undetectable in this study. A tempo-spatial variation of geranyl β -primeveroside abundance in tea plants existed, with high levels in young and green tissues and low levels in mature or non-green tissues. Cytosolic CsNUDX1-cyto showed higher hydrolysis activity of geranyl-pyrophosphate to geranyl-monophosphate (GP) in vitro than did chloroplastidial CsNUDX1-chlo. A transgenic study revealed that expression of CsNUDX1-cyto resulted in significantly more geranyl β -primeveroside in transgenic Nicotiana benthamiana compared with non-transgenic wild-type, whereas expression of CsNUDX1-chlo had no effect. An antisense oligo-deoxynucleotide study confirmed that suppression of CsNUDX1-cyto transcription in tea shoots led to a significant decrease in geranyl β -primeveroside abundance. Additionally, CsNUDX1-cyto transcript levels and geranyl β -primeveroside abundances shared the same tempo-spatial patterns in different organs in the tea cultivar "Shucha Zao," indicating that CsNUDX1-cyto is important for geranyl β -primeveroside formation in tea plants. Results also suggested that neither of the two candidate GES genes in tea plants did not function as GES in transgenic N. benthamiana. All our data indicated that CsNUDX1-cyto is involved in geranyl β -primeveroside production in tea plants. Our speculation about possible conversion from the chemical product of CsNUDX1-cyto to geranyl β -primeveroside in plants was also discussed. [ABSTRACT FROM AUTHOR]

Summary: Food fraud causes significant economic losses for the industry and generates distrust between the consumers and traders. Tea is one of the most valued beverages throughout the world, being vulnerable to economically motivated cheat. The objective of the study was to develop the potential of hyperspectral imaging (HSI) allying multivariate analysis and data fusion to identify the authenticity of Keemun black tea quality categories. Data fusion that integrated of texture characteristics based on grey level co‐occurrence matrix and visible and near‐infrared spectral features via competitive adaptive reweighted sampling (CARS) was as the target data for modelling. Support vector machine (SVM) and random forest (RF) were utilised for the classification of tea samples of seven grades. The RF model using fused data gave the best performance with the correct discriminant rate of 92.70% for the prediction set. This study demonstrated that HSI coupled with RF was effective in identifying tea sample rank. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least‐squares discriminant analysis (PLS‐DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near‐infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA‐SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

Tea quality is often evaluated by experienced tea tasters; however, their assessments are subjective, being influenced by their individual physiological and psychological condition. Herein, we fabricated a colorimetric sensor array-based artificial olfactory system for sensing the quality of Chinese green tea. First, the colorimetric sensors array was man-made using printing 12 chemically responsive dyes (9 porphyrins, metalloporphyrins and 3 pH indicators) on silica-gel flat plate. The plate was exposed to volatile organic compounds, and the colour changes in each sample were obtained by distinguishing between the images of sensor array before and after contact with tea sample. The values of colour composition changes were extracted from the dyes’ colour sections. Multivariate calibrations were applied through principal component analysis and back propagation artificial neural network (BP-ANN) for modelling. The optimum BP-ANN model was obtained with nine principal components, and the discrimination rate was equal to 85% and 86% in the calibration and prediction sets, respectively. We thus conclude that the low cost colorimetric sensor array-based artificial olfactory technique has great potential for application in intelligent evaluation of the quality of green tea. [ABSTRACT FROM AUTHOR]

A total 206 samples of green, yellow, white, black, and Oolong teas were utilized to acquire hyperspectral imaging, and five tea categories were identified based on visible and near-infrared (NIR) hyperspectral imaging, combined with classification pattern recognition. The characteristic spectra were extracted from the region of interest (ROI), and the standard normal variate (SNV) method was preprocessed to reduce background noise. Four dominant wavelengths (589, 635, 670, and 783 nm) were selected by principal component analysis (PCA) as spectral features. Textural features were extracted by the Grey-level co-occurrence matrix (GLCM) from images at selected dominant wavelengths. Linear discriminant analysis (LDA), library support vector machine (Lib-SVM), and extreme learning machine (ELM) classification models were established based on full spectra, spectral features, textural features, and data fusion, respectively. Lib-SVM was the best model with the input data fusion, and the correct classification rate (CCR) achieved 98.39%. The results implied that visible and NIR hyperspectral imaging combined with Lib-SVM has the capability of rapidly and non-destructively classifying tea categories. [ABSTRACT FROM AUTHOR]

Near-infrared spectroscopy and back propagation-artificial neural network (BP-ANN) model in conjunction with synergy interval partial least squares (siPLS) algorithm were used to evaluate tea shoots quality. The near-infrared spectra regions relevant to tea quality (12493 cm−1to 11645 cm−1, 9087.5 cm−1to 8242.7 cm−1, 8238.9 cm−1to 7394.2 cm−1, and 6541.7 cm−1to 5697 cm−1) were selected using siPLS algorithm. The two principal components that explained 99.46% of the variability in this spectral data were then used to calibrate the BP-ANN quality index (QI) model. The performance of this model [the coefficient of determination for prediction (), 0.9680; root mean square error of prediction (RMSEP), 0.0178] was superior to those of the BP-ANN model (= 0.9332, RMSEP = 0.0285) and the siPLS model (= 0.9230, RMSEP = 0.0360). The predicted QI values of 25 samples highly correlated with the experimental values (= 0.9223, RMSEP = 0.0344). The QI model with the combined siPLS-BP-ANN algorithms accurately predicted the quality of tea shoots. [ABSTRACT FROM PUBLISHER]

• Assessing tea rank by Vis-NIR spectra and a hybrid variable selection strategy. • Feature data are selected by the GA, IRIV, VCPA, VCPA-GA, and VCPA-IRIV. • Authentication models for tea ranks are created using LSSVM, RF, and BPNN. • The VCPA-IRIV-LSSVM model obtains superior performance. Tea is one of the most consumed and popular beverages around the world. Authenticating tea quality is essential to resist counterfeit goods and prevent fraud. This study innovatively aims to use a visible-near infrared (Vis-NIR) sensor combined with a variable combination population analysis (VCPA)-based hybrid variable selection algorithm to realize the authentication of tea quality. First, the spectra of 700 tea samples from seven different grades are acquired using a Vis-NIR sensor. Second, the raw spectra acquired are preprocessed by Savitzky-Golay convolution smoothing combined with first derivative (SG1), standard normal variate transformation (SNV), and multiplicative scatter correction (MSC), and the best pretreating technique is determined by support vector machine (SVM) algorithm. Third, the VCPA-based hybrid method optimizes the variable space of the optimal pretreated spectra data in the first step, and a genetic algorithm (GA) and iteratively retaining informative variables (IRIV) are utilized to execute further optimization and shrink the variable in the second step. Finally, authentication models of tea quality are constructed employing least squares support vector machine (LSSVM), random forest (RF), and back propagation neural network (BPNN) approaches based on the selected variables. The results show that the VCPA-IRIV-LSSVM model obtains the best predictive performance with an accuracy of 97.40% in the predictive process. Our findings demonstrate that it is feasible to apply the inexpensive vis-NIR sensor to assess quality attributes in tea. Also, the VCPA-based hybrid method can search the combination of the optimal wavelength and improve the identification accuracy and robustness of the model and can assist sensor manufacturers in developing a cheap and user-friendly smartphone-coupled micro spectroscopic sensor for authentication of tea quality. [ABSTRACT FROM AUTHOR]

To address the problem of the lack of rapid discrimination technology for comprehensive evaluation of product quality and grade in the field of tea quality control, this study uses near-infrared spectroscopy, electronic eye, electronic tongue, and electronic nose technology to sense the external and internal quality sensing characteristics of tea leaves, and realizes comprehensive evaluation of four quality factors such as "color, aroma, taste and shape" of tea leaves through cross-sensor multimodal quality characteristics data fusion. For the first time, a comprehensive method for assessing the grade quality of tea based on deep learning algorithms combining cross-sensor multimodal fusion features such as color, texture, shape, optical, electronic tongue, and electronic nose is established. The results showed that the constructed deep convolutional neural network (CNN) classification model had a misclassification rate of only 0.86% in the prediction set, which was better than the modeling results based on traditional classification methods. This study reveals that the CNN algorithm can effectively evaluate the quality grade of tea samples and realize the digital evaluation of multiple evaluation factors of tea sensory quality. [Display omitted] • Description of tea quality using deep learning and multi-sensor feature fusion. • A comprehensive evaluation for tea "color, aroma, taste and shape" is proposed. • The constructed CNN model has a misclassification rate of only 0.86%. [ABSTRACT FROM AUTHOR]

• A productive equipment for sorting famous tea raw materials was designed. • The sorting scheme consists of classification, control and process scheme. • Higher grades of fresh tea leaves have a longer horizontal throwing trajectory. • The sorting effect of high-grade raw material is better under the optimal parameters. • Improving the classification performance of the model using genetic algorithms. Famous tea is a pillar of China's tea industry and must be processed using fresh leaves of consistent tenderness and size. However, equipment with high accuracy and efficiency are unavailable for sorting fresh tea leaves (FTLs). Therefore, in this study, we designed a machine-vision platform for sorting FTLs. This platform subjects FTLs to a horizontal tossing motion and change trajectory by blowing air, thereby achieving the sorting of different types of FTLs. The overall sorting scheme consists of classification scheme, control scheme, and process scheme. We found that different types of FTLs have different flight trajectories when subjected to horizontal tossing. And have different optimal classification features and sorting parameters. When sorting high-grade FTLs (low-grade FTLs), the classification feature used is area and mean saturation (equivalent diameter, major-axis length and G variance), the designed platform achieved a recognition rate, purity, selection rate, and integrity rate of 100% (98.5%), 94.32% (87.47%), 91.67% (90.67%), and 100% (100%), respectively. Under optimal parameters, the production efficiency of the platform reached 25 kg/h. Overall, the results of this study indicate that the designed platform can meet the small-scale production requirements for famous tea. [ABSTRACT FROM AUTHOR]

[Display omitted] • Digital diagnosis of Dianhong black tea quality using pocket-sized NIR. • Optimizing features by the PSO, ACO, SA, and GWO. • Recognition models for black tea categories were developed using ELM, PLS-DA, and SVM combined with the selected features. • The GWO-SVM model achieves the expected predictive performance. This paper investigates the feasibility of combining portable near-infrared (NIR) spectroscopy with chemometric algorithms to improve the quality prediction performance of Dianhong black tea. This study presented a pocket-sized NIR device (defined as NIR-S-R2) for the simultaneous identification of multiple categories of Dianhong black tea. Firstly, the spectral information of 700 multi-category Dianhong tea samples was sensed by the NIR-S-R2 system and high-quality analytical spectra of the samples were obtained by comparing multiple spectral pre-processing algorithms. Then, based on the best pre-processed spectral data, four characteristic wavelength evolution algorithms, namely particle swarm optimization, ant colony optimization, simulated annealing, and grey wolf optimization (GWO), were employed to obtain the characteristic spectral variables. Finally, extreme learning machine, partial least squares discriminant analysis, and support vector machine (SVM) methods were adopted to develop recognition models for seven categories of Dianhong black tea based on the selected features. The results showed that the GWO-SVM model had the best predictive capability, with a prediction accuracy of 91.42%. At suitable spectral wavelengths, the NIR-S-R2 system combined with chemometrics offers good discriminatory accuracy and potential for the application of extended detection. [ABSTRACT FROM AUTHOR]

• A novel solution phase CSA was used for monitoring black tea fermentation. • UV-visible spectroscopy was employed to obtain CSA features in real time. • The accuracy of CARS-SVM model for discriminating fermentation degree was 100%. • Accurate prediction models for catechins and theaflavins were obtained. Rapid monitoring of fermentation quality has been the key to realizing the intelligent processing of black tea. In our study, mixing ratios, sensing array components and reaction times were optimized before an optimal solution phase colorimetric sensor array was constructed. The characteristic spectral information of the array was obtained by UV–visible spectroscopy and subsequently combined with machine learning algorithms to construct a black tea fermentation quality evaluation model. The competitive adaptive reweighting algorithms (CARS)-support vector machine model discriminated the black tea fermentation degree with 100% accuracy. For quantification of catechins and four theaflavins (TF, TFDG, TF-3-G, and TF-3′-G), the correlation coefficients of the CARS least square support vector machine model prediction set were 0.91, 0.86, 0.76, 0.72 and 0.79, respectively. The results obtained within 2 min enabled accurate monitoring of the fermentation quality of black tea, which provides a new method and idea for intelligent black tea processing. [ABSTRACT FROM AUTHOR]

• NIR-HSI was used for quality monitoring of fermentation process of black tea. • Qualitative and quantitative models were developed to assess the quality. • Spatial distribution of catechins during fermentation was mapped by HSI. • Noncontact evaluation for intelligent processing of black tea was achieved. Fermentation is the most critical step in black tea processing and contributes to the taste and appearance of black tea. Conventionally, the degree of fermentation is evaluated through sensory reviews by experts. However, this method has low accuracy and is subject to reviewers' experience. In this study, the fermentation quality of black tea was qualitatively and quantitatively evaluated through near-infrared hyperspectral imaging (HSI). The spectral information of samples of four fermentation degrees was collected and preprocessed using standard normal variate. The developed qualitative model could discriminate fermentation degrees, with a correct classification rate of 83.33 % for the prediction set. The quantitative prediction model achieved excellent prediction accuracy for the catechin content, with a residual prediction deviation of 2.18. Finally, the spatial distribution of catechins during fermentation was mapped through chemical imaging. Our study confirmed the feasibility of HSI for monitoring the fermentation quality of black tea. [ABSTRACT FROM AUTHOR]

Polyphenols are the key taste and health components of tea. Typically, chemical analysis is used to determine the total polyphenol (TP) content of tea. However, this process is time consuming. In this study, the TP content of six tea types, namely green, white, yellow, oolong, black, and dark, was assessed using near-infrared hyperspectral imaging. Here, 100% accuracy was achieved for both the calibration and prediction sets for qualitative discrimination of tea by using the principal component analysis-K-nearest neighbor model. Important wavelengths of TP were selected using the regression coefficients (RCs) of the partial least squares regression (PLSR) model. The proposed RC-PLSR model yielded satisfactory prediction results with a residual predictive deviation of 3.34. The differences in the spatial distribution of TP in various tea samples were visualized using distribution maps. This study provided a rapid and nondestructive method for the identification of tea types and the visualization of the TP content of tea. • Considerable differences in TP content were analyzed in different types of tea. • PCA-KNN model yielded accurate discriminations of tea types. • RC-PLSR model gave accurate prediction of the total polyphenols in different teas. • Distribution maps of total polyphenols content in intact tea samples were obtained. [ABSTRACT FROM AUTHOR]

Withering of leaves is an important step in the processing of black tea and determines the taste and aroma of the tea. Currently, the withering degree of the leaves is mainly determined via sensory evaluation by the tea maker, and this is time-consuming and laborious. In this study, near-infrared spectroscopy, electronic eye, and colorimetric sensing array technologies were combined to evaluate the extent of withering. A low-cost micro-spectrometer, a self-built machine vision system, and a homemade colorimetric array were used to capture relevant information regarding the withered tea leaf samples in situ. Additionally, low- and middle-level data fusion strategies were modeled and compared using a support vector machine (SVM). The SVM model, which combined the information obtained using the three technologies, performed substantially better than a single technology. Low-level fusion achieved acceptable discriminant performance, with an accuracy of 90.00% for both the SVM and principal component analysis–SVM models for the prediction set. The middle-level fusion strategy achieved better performance than the low-level fusion strategy, with an optimal discriminant accuracy of 97.50% for SVM model. Hence, this study demonstrated that in situ and low-cost quality assessment and intelligent control of the withering process of black tea leaves are possible. [Display omitted] • E-nose, E-eye and NIRS were jointly used to monitor withering degree of black tea. • Low- and middle-level data fusion were critically used and compared. • Performance of data fusion in middle-level outperformed that of independent data. • It provides a low-cost, in situ method for quality monitoring of tea processing. [ABSTRACT FROM AUTHOR]

• Assessing black tea quality by Vis-NIR spectra. • Selecting spectral features by the Si-PLS, CARS, iVISSA, and VISSA. • Chemometric models for black tea quality developed using SVM-based kernel methods. • The CARS-linear kernel SVM model shows promising results. Current evaluation systems for tea quality assessment are difficult to use and unstable. A rapid and cheap assessment method is required to distinguish the quality levels of tea. In this study, a visible-near-infrared (Vis–NIR) spectrometer and support vector machine (SVM)-based kernels were used for the qualitative categorization of black tea. First, a Vis–NIR system was used to acquire the spectral data of seven levels of tea samples. The obtained spectra were preprocessed using the Savitzky–Golay smoothing combined with the first derivative and standard normal variate transformation. Then, four characteristic wavelength selection algorithms, namely synergy interval partial least square, competitive adaptive reweighted sampling (CARS), the variable iterative space shrinkage approach (VISSA), and the interval VISSA, were used to obtain preprocessed spectral features. Finally, the SVM was used with four kernel functions, namely the linear, Gaussian, quadratic, and cubic functions, to develop models based on the variables obtained from the selected features for tea quality classification. The results revealed that the CARS–linear kernel SVM model exhibited the best results, with a correct identification rate of 91.85 % in the validation process. Our findings demonstrate that Vis-NIR spectroscopy can be a rapid, inexpensive, efficient, and alternative method for predicting the quality of black tea. [ABSTRACT FROM AUTHOR]

• Accumulation of specific phenolic compounds in Siqiu and Gujing tea leaves. • Deficient promoter of F3′5′H leads to low accumulation of trihydroxy flavonoids. • Proanthocyanidins and galloylquinic acid is correlated to astringency intensity. Wild tea plants, which are classified into different species in the section Thea of the genus Camellia , are widely distributed in southern China. Tea produced from these plants has a unique flavor, which is different from that of tea produced from tea cultivars. In this study, we performed a comparative analysis of morphology, phylogenetic relationships, and phenolic compound metabolism between two wild tea plants (Gujing and Siqiu) and a tea cultivar (Shuchazao). Siqiu and Gujing tea plants had similar morphological traits and could be phylogenetically classified into a same cluster, which was entirely separate from the cluster containing widely cultivated cultivars such as Camellia sinensis cv. Shuchazao. Combined metabolomic and transcriptome analyses revealed that UGT84a22 was highly expressed in Gujing leaves compared with Shuchazao and Siqiu leaves, which may lead to the high accumulation of galloylquinic acid in Gujing leaves. A 14-bp deletion spanning the −765–(−7 5 1) range in the F3′5′H promoter potentially led to low F3′5′H expression levels in Siqiu and Gujing tea plants, which severely disrupted the accumulation of trihydroxy flavonoids in Gujing and Siqiu tea leaves. The high astringency intensity in Gujing tea could be due to the high accumulation of proanthocyanidins and galloylquinic acid. The results of the present study may improve our understanding of the metabolic characteristics of each evolutionary group of species or varieties in the section Thea of the genus Camellia. [ABSTRACT FROM AUTHOR]

• We firstly used CDs to identify eight grades of Dianhong black tea. • We firstly increased recognition rate by adding cobalt ions. • All materials we used are laboratory-synthesized, and operation is convenient. We established a novel Dianhong black tea grades discriminant analytic technique based on a fluorescence image along with carbon quantum dots (CDs) as fluorescent probes. Different grades of Dianhong black tea contain different various amounts of tea polyphenols. Tea polyphenols can quench the fluorescent intensity of CDs, resulting in different fluorescent peaks; Dianhong black tea grades can then be discriminated through the use of principal component analysis and Bayesian analysis. Compared with the additional data processing required in other methods, the advantage of our method is that the fluorescence curve can be used directly, and it achieves satisfactory results. We firstly used CDs combined with chemometrics to identify eight grades of Dianhong black tea, and we also provide a new method that improves the identification rate using nanotechnology to avoid performing complex data processing. [ABSTRACT FROM AUTHOR]