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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.)

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., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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.)

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.)

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.)

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., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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 (R p ) 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., (© 2020 Society of Chemical Industry.)

Tea plant is an important economic crop, which is used to produce the world's oldest and most widely consumed tea beverages. Here, we present a high-quality reference genome assembly of the tea plant (Camellia sinensis var. sinensis) consisting of 15 pseudo-chromosomes. LTR retrotransposons (LTR-RTs) account for 70.38% of the genome, and we present evidence that LTR-RTs play critical roles in genome size expansion and the transcriptional diversification of tea plant genes through preferential insertion in promoter regions and introns. Genes, particularly those coding for terpene biosynthesis proteins, associated with tea aroma and stress resistance were significantly amplified through recent tandem duplications and exist as gene clusters in tea plant genome. Phylogenetic analysis of the sequences of 81 tea plant accessions with diverse origins revealed three well-differentiated tea plant populations, supporting the proposition for the southwest origin of the Chinese cultivated tea plant and its later spread to western Asia through introduction. Domestication and modern breeding left significant signatures on hundreds of genes in the tea plant genome, particularly those associated with tea quality and stress resistance. The genomic sequences of the reported reference and resequenced tea plant accessions provide valuable resources for future functional genomics study and molecular breeding of improved cultivars of tea plants., (Copyright © 2020 The Authors. Published by Elsevier Inc. 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.)

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., (Published by Elsevier Ltd.)

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.)

Tea is a globally consumed non-alcohol beverage with great economic importance. However, lack of the reference genome has largely hampered the utilization of precious tea plant genetic resources towards breeding. To address this issue, we previously generated a high-quality reference genome of tea plant using Illumina and PacBio sequencing technology, which produced a total of 2,124 Gb short and 125 Gb long read data, respectively. A hybrid strategy was employed to assemble the tea genome that has been publicly released. We here described the data framework used to generate, annotate and validate the genome assembly. Besides, we re-predicted the protein-coding genes and annotated their putative functions using more comprehensive omics datasets with improved training models. We reassessed the assembly and annotation quality using the latest version of BUSCO. These data can be utilized to develop new methodologies/tools for better assembly of complex genomes, aid in finding of novel genes, variations and evolutionary clues associated with tea quality, thus help to breed new varieties with high yield and better quality in the future.

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: 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 (R P ) 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., (© 2018 Society of Chemical Industry.)

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., (© 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.)

High levels of fluoride in tea plants pose a potential health risk to humans who drink tea. It has been demonstrated that tea plant fluoride is closely related to the available fluoride in soil. But approaches that could be used to regulate the availability of fluoride in soil have been rarely seen. This study aims to investigate how the addition of charcoal and bamboo charcoal affected soil fluoride availability and bioaccumulation of fluoride in tea plants. In a microcosm experiment, tea plants were grown in the tea garden soil mixed with different amounts of charcoal and bamboo charcoal [that is, 0.5, 1.0, 2.5, and 5.0 % (w/w)]. Soil-fluoride fractions and fluoride accumulated in tea plants were determined using the sequential extraction and ion selective electrode method. Obtained results showed that both charcoal and bamboo charcoal additions significantly enhanced the concentrations of Fe/Mn oxide-bound fluoride, but significantly reduced the concentrations of water-soluble and exchangeable fluoride (p < 0.05) in soil. Charcoal and bamboo charcoal additions also significantly decreased the amounts of fluoride in tea roots and tea leaves (p < 0.05). However, the additions of charcoal and bamboo charcoal had no impacts on the tea quality, as indexed by the concentrations of polysaccharides, polyphenols, amino acids, and caffeine in tea leaves. These results suggested that application of charcoal and bamboo charcoal may provide a useful method to reduce the availability of fluoride in soil and the subsequent fluoride uptake by tea plants.

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]

Abstract: Near-infrared spectroscopy (NIRS) combined with chemometric tools was utilized as a rapid analysis method to assess quality and to differentiate geographical origins of black tea. A partial least squares (PLS) algorithm was employed for the calibration of models predicting the levels of caffeine, water extract, total polyphenols, and free amino acids, while a factorization method was proposed to trace black tea from different geographical origins. In the calibration set, the root mean squared error of cross validation (%) and the correlation coefficient (R) for caffeine, water extracts, total polyphenols and free amino acids were 0.102%, 0.654%, 0.552%, and 0.248% and 0.983, 0.977, 0.975, and 0.943, respectively. In the prediction set, the root mean squared error of prediction and R for the corresponding constituents were 0.160%, 0.685%, 0.594%, and 0.273% and 0.955, 0.962, 0.954, and 0.927, respectively. The identification accuracy for black tea from different geographical origins reached 94.3%. This study demonstrated that NIR spectroscopy can be successfully applied to rapidly determine the main chemical compositions and geographical origins of black tea. [Copyright &y& Elsevier]

• 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]

A simple, rapid and low-cost analytical method was employed for simultaneous determination of bioactive constituents and antioxidant capability of green tea. The strategy was based on swarm intelligence algorithms with partial least squares (PLS) such as simulated annealing PLS (SA-PLS), ant colony optimization PLS (ACO-PLS), genetic algorithm PLS (GA-PLS), and synergy interval PLS (Si-PLS) coupled with Near-infrared (NIR) spectroscopy. These algorithms were independently applied to select informative spectral variables and improve the prediction of green tea components. Results showed that NIR combined with SA-PLS and Si-PLS had a strong correlation coefficient with the wet-chemical methods for predicting epigallocatechin gallate (R p 2 = 0.97); epigallocatechin (R p 2 = 0.97); epicatechin gallate (R p 2 = 0.96); epicatechin (R p 2 = 0.91); catechin (R p 2 = 0.98); caffeine (R p 2 = 0.96); theanine (R p 2 = 0.93); and antioxidant capability (R p 2 = 0.80) in green tea. Our results revealed the potential utilization of NIR spectroscopy coupled with SA-PLS and Si-PLS algorithms as an effective and robust technique to simultaneously predict active constituents and antioxidant capability of green tea. • Swarm intelligence algorithms were applied to predict green tea constituents. • Tea bioactive compounds and antioxidant capability models were improved. • PLS, enhanced by Si, GA, SA and ACO proved feasible to quantify tea constituents. • NIR coupled with SA-PLS and Si-PLS exhibited best predictive ability. [ABSTRACT FROM AUTHOR]