Search

Cold stress causes considerable damage to tender tea seedlings. Previous studies have explored changes in the physiological and biochemical factors of tea in response to cold stress; however, the mechanisms of cold resistance in ancient tea tree plants are unclear. The aim of this study was to analyze the effects of 0 °C cold stress for 15 days and 24 °C ambient temperature recovery for 5 days on the physiological and biochemical characteristics of two representative old tea varieties: Dali tea and Siqiu tea. The results revealed significant changes in antioxidant, photosynthetic efficiency, and physiological and biochemical indicators in response to cold stress, with the two species exhibiting different patterns. Cold stress decreased chlorophyll and carotene content, Fv/Fm, Y(II), non-photochemical quenching coefficient, photochemical quenching, and superoxide dismutase (SOD) activity, and increased intercellular CO2 concentration and ascorbate peroxidase activity. Siqiu tea showed a higher increase in soluble sugar content and antioxidant enzyme activity and a lower accumulation of malondialdehyde and minimal fluorescence (F0) than Dali, indicating a greater tolerance to cold stress. Based on partial least-squares discriminant analysis, six key differential physiological indicators of cold resistance—water-soluble sugar, F0, peroxidase, catalase, SOD, and gas conductance—were identified. Our findings provide technical support for identifying ways to protect ancient tea trees from extreme weather conditions. [ABSTRACT FROM AUTHOR]

Aroma is an indispensable factor that substantially impacts the quality assessment of black tea. This study aims to uncover the dynamic alterations in the sweet and floral aroma black tea (SFABT) throughout various manufacturing stages using a comprehensive analytical approach integrating gas chromatography electronic nose, gas chromatography–ion mobility spectrometry (GC-IMS), and gas chromatography–mass spectrometry (GC-MS). Notable alterations in volatile components were discerned during processing, predominantly during the rolling stage. A total of 59 typical volatile compounds were identified through GC-IMS, whereas 106 volatile components were recognized via GC-MS throughout the entire manufacturing process. Among them, 14 volatile compounds, such as linalool, β-ionone, dimethyl sulfide, and 1-octen-3-ol, stood out as characteristic components responsible for SFABT with relative odor activity values exceeding one. This study serves as an invaluable theoretical platform for strategic controllable processing of superior-quality black tea. [ABSTRACT FROM AUTHOR]

Vitamin C, a water-soluble vitamin with strong reducing power, cannot be synthesized by the human body and participates in a variety of important biochemical reactions. Vitamin C is widely used in the pharmaceutical, food, health care, beverage, cosmetics, and feed industries, with a huge market demand. The classical two-step fermentation method is the mainstream technology for vitamin C production. D-sorbitol is transformed into L-sorbose by Gluconobacter oxydans in the first step of fermentation; then, L-sorbose is transformed into 2-keto-L-gulonic acid (2-KGA) by a coculture system composed of Ketogulonicigenium vulgare and associated bacteria; and finally, 2-KGA is transformed into vitamin C through chemical transformation. The conversion of L-sorbose into 2-KGA in the second fermentation step is performed by K. vulgare. However, considering the slow growth and low 2-KGA production of K. vulgare when cultured alone, it is necessary to add an associated bacteria to stimulate K. vulgare growth and 2-KGA production. Although the mechanism by which the associated bacteria promote K. vulgare growth and 2-KGA production has extensively been studied, this remains a hot topic in related fields. Based on the latest achievements and research, this review summarizes the metabolic characteristics of K. vulgare and associated bacteria and elucidates the mechanism by which the associated bacteria promote the growth and 2-KGA production of K. vulgare. [ABSTRACT FROM AUTHOR]

Tea's biochemical compounds and flavor quality vary depending on its grade ranking. Dianhong Congou black tea (DCT) is a unique tea category produced using the large-leaf tea varieties from Yunnan, China. To date, the flavor characteristics and critical components of two grades of high-quality DCT, single-bud-grade DCT (BDCT), and special-grade DCT (SDCT) manufactured mainly with single buds and buds with one leaf, respectively, are far from clear. Herein, comparisons of two grades were performed by the integration of human sensory evaluation, an electronic tongue, chromatic differences, the quantification of major components, and metabolomics. The BDCT possessed a brisk, umami taste and a brighter infusion color, while the SDCT presented a comprehensive taste and redder liquor color. Quantification analysis showed that the levels of total polyphenols, catechins, and theaflavins (TFs) were significantly higher in the BDCT. Fifty-six different key compounds were screened by metabolomics, including catechins, flavone/flavonol glycosides, amino acids, phenolic acids, etc. Correlation analysis revealed that the sensory features of the BDCT and SDCT were attributed to their higher contents of catechins, TFs, theogallin, digalloylglucose, and accumulations of thearubigins (TRs), flavone/flavonol glycosides, and soluble sugars, respectively. This report is the first to focus on the comprehensive evaluation of the biochemical compositions and sensory characteristics of two grades of high-quality DCT, advancing the understanding of DCT from a multi-dimensional perspective. [ABSTRACT FROM AUTHOR]

The moisture content of black tea is an important factor affecting its suitability for processing and forming the unique flavor. At present, the research on the moisture content of black tea mainly focuses on a single withering step, but the research on the rapid detection method of moisture content of black tea applicable to the entire processing stage is ignored. This study is based on a miniaturized near-infrared spectrometer(micro−NIRS) and establishes the prediction models for black tea moisture content through machine learning algorithms. We use micro−NIRS for spectroscopic data acquisition of samples. Linear partial least squares (PLS) and nonlinear support vector regression (SVR) were combined with four spectral pre−processing methods, and principal component analysis (PCA) was applied to establish the predictive models. In addition, we combine the gray wolf optimization algorithm (GWO) with SVR for the prediction of moisture content, aiming to establish the best prediction model of black tea moisture content by optimizing the selection of key parameters (c and g) of the kernel function in SVR. The results show that SNV, as a method to correct the error of the spectrum due to scattering, can effectively extract spectral features after combining with PCA and is better than other pre−processing methods. In contrast, the nonlinear SVR model outperforms the PLS model, and the established mixed model SNV−PCA−GWO−SVR achieves the best prediction effect. The correlation coefficient of the prediction set and the root mean square error of the prediction set are 0.9892 and 0.0362, respectively, and the relative deviation is 6.5001. Experimental data show that the moisture content of black tea can be accurately and effectively determined by micro-near-infrared spectroscopy. [ABSTRACT FROM AUTHOR]

Moisture content (MC) is an important indicator to monitor the quality of Longjing tea during processing; therefore, it becomes more critical to develop digital moisture content detection methods for processing. In this study, based on a micro-near infrared (NIR) spectrometer and portable colorimeter, we used Longjing tea under the full processing process as the research object, and used competitive adaptive reweighted sampling (CARS) and a principal component analysis (PCA) to extract characteristic bands of spectral data as well as the principal component reduction processing of the color difference and glossiness data, respectively, combined with sensor data fusion technology to establish a quantitative prediction model of the partial least squares (PLS) for the moisture content of Longjing tea. The PLS quantitative moisture content prediction model, based on middle-level data fusion, obtained the best prediction accuracy and model robustness, with the correlation coefficient of the prediction set ( R p ) and the root mean square error of prediction (RMSEP) being 0.9823 and 0.0333, respectively, with a residual predictive deviation (RPD) of 6.5287. The results indicate that a data fusion of a micro NIR spectrometer and portable Colorimeter is feasible to establish a quantitative prediction model of the moisture content in Longjing tea processing, while multi-sensor data fusion can overcome the problem of a low prediction accuracy for the model established by single sensor data. More importantly, data fusion based on low-cost, fast, and portable detection sensors can provide new ideas and methods for real-time online detection in Longjing tea in actual production. [ABSTRACT FROM AUTHOR]

Fixation is the most critical step in the green tea process. Hence, this study developed a rapid and accurate moisture content detection for the green tea fixation process based on near-infrared spectroscopy and computer vision. Specifically, we created a quantitative moisture content prediction model appropriate for the processing of green tea fixation. First, we collected spectrum and image information of green tea fixation leaves, utilizing near-infrared spectroscopy and computer vision. Then, we applied the partial least squares regression (PLSR), support vector regression (SVR), Elman neural network (ENN), and Elman neural network based on whale optimization algorithm (WOA-ENN) methods to build the prediction models for single data (data from a single sensor) and mid-level data fusion, respectively. The results revealed that the mid-level data fusion strategy combined with the WOA-ENN model attained the best effect. Namely, the prediction set correlation coefficient (Rp) was 0.9984, the root mean square error of prediction (RMSEP) was 0.0090, and the relative percent deviation (RPD) was 17.9294, highlighting the model's excellent predictive performance. Thus, this study identified the feasibility of predicting the moisture content in the process of green tea fixation by miniaturized near-infrared spectroscopy. Moreover, in establishing the model, the whale optimization algorithm was used to overcome the defect whereby the Elman neural network falls into the local optimum. In general, this study provides technical support for rapid and accurate moisture content detection in green tea fixation. [ABSTRACT FROM AUTHOR]

Yunnan Congou black tea (YCBT) is a typical black tea in China, and is rich in theaflavins (TFs), thearubigins (TRs), and theabrownins (TBs). However, the influence of the fermentation temperature and time on the liquor and appearance color and the correlation between the tea pigments and its color attributes remain unclear. We investigated the effects of the fermentation temperature and time on the color attributes and tea pigments of YCBT. A low fermentation temperature was beneficial to maintain a bright orange-red liquor color and promote the accumulation of TFs and TRs. In contrast, a high temperature gave the liquor a glossy appearance and was beneficial for the formation of TBs. A correlation analysis showed that the 10TFRB index best represented the contribution of tea pigments to the quality of black tea. Moreover, TRs and TBs content prediction models were established based on the liquor L and H values, where the former value can be used as an important index to judge the fermentation process. This study will further enrich the theory of black tea processing chemistry and provide technical support for the precise and directional production of black tea. [ABSTRACT FROM AUTHOR]

The characteristic aroma of Chinese black tea (BT) produced in summer usually lacks intensity and persistence, reducing consumer acceptance and market demand. Strobilanthes tonkinensis Lindau (STL) possesses excellent biological characteristics, making it a promising novel tea ingredient. We investigated the effects of different addition methods and concentrations for the novel additive STL on the sensory quality of BT. A 20:1500 g/g addition to rolled tea leaves was identified as the best BT with STL (BoS) treatment. We identified 141 volatile metabolites (VMs) for the first time in STL, with high alcohol, ester, ketone, enyne, alkyne, and alkane contents. Partial least-squares discriminant analysis distinguished the samples and revealed 28, 26, and 14 differential VMs in STL vs. BoS, BT vs. STL, and BT vs. BoS comparisons, respectively. Using a combination of Venn, multiple experiment viewer, and odor activity value analysis, 16 key differential VMs were identified. Compared to BT, the 1-octen-3-ol, 1-hexanol, 1-dodecanol, (E)-3-hexen-1-ol, phenylethyl alcohol, and 2-methoxy-3-(2-methylpropyl)-pyrazine contents in BoS were 394.7%, 53.6%, 34.1%, 24.4%, 9.9%, and 5.7% higher, respectively. BoS combined the aromatic properties of BT and STL into a sweet and glutinous rice flavor. The results provide theoretical support for future research and development of novel BT-based products. [ABSTRACT FROM AUTHOR]

Converting biomass into high value-added compounds has attracted great attention for solving fossil fuel consumption and global warming. 5-Hydroxymethylfurfural (HMF) has been considered as a versatile biomass-derived building block that can be used to synthesize a variety of sustainable fuels and chemicals. Among these derivatives, 2,5-furandicarboxylic acid (FDCA) is a desirable alternative to petroleum-derived terephthalic acid for the synthesis of biodegradable polyesters. Herein, to fully understand the current development of the catalytic conversion of biomass to FDCA, a comprehensive review of the catalytic conversion of cellulose biomass to HMF and the oxidation of HMF to FDCA is presented. Moreover, future research directions and general trends of using biomass for FDCA production are also proposed. [ABSTRACT FROM AUTHOR]

The sweet-mellow taste sensation is a unique and typical feature of premium congou black tea infusions. To explore the key taste-active compounds that influence the sweet-mellow taste, a sensory and molecular characterization was performed on thirty-three congou black tea infusions presenting different taste qualities, including the sweet-mellow, mellow-pure, or less-mellow taste. An integrated application of quantitative analysis of 48 taste-active compounds, taste contribution analysis, and further validation by taste supplementation experiments, combined with human sensory evaluation revealed that caffeine, γ-aminobutyric acid, rutin, succinic acid, citric acid, and gallic acid negatively affect the sweet-mellow taste, whereas glucose, sucrose, and ornithine positively contribute to the sweet-mellow taste of congou black tea infusions. Particularly, rutin, γ-aminobutyric acid, gallic acid, and caffeine, which impart the major inhibitory effect to the manifestation of the sweet-mellow taste, were identified as the key influencing components through stepwise screening and validation experiments. A modest level of these compounds was found to be favorable for the development and manifestation of the sweet-mellow taste. These compounds might potentially serve as the regulatory targets for oriented-manufacturing of high-quality sweet-mellow congou black tea. [ABSTRACT FROM AUTHOR]

Background : Sleep-related breathing disorders (SRBD) have been identified as a major public health problem closely related to adolescent obesity. We aimed to estimate the prevalences of SRBD and snoring in adolescents in Changchun City, Northeastern China, and to evaluate the associated factors in this population. Methods: In total, 1955 adolescents aged 11-18 years were recruited in Changchun City using stratified cluster sampling. Parents and caretakers of children completed the questionnaires, which included demographic characteristics, anthropometric parameters and a pediatric sleep questionnaire (SRBD scale). Logistic regression was used to analyze the relationship between SRBD, snoring and other factors. Results: The prevalences of SRBD and snoring in our population were 3.7% and 3.3%, respectively, and the prevalences of overweight and obesity were 12.6% and 4.9%. Multivariate logistic regression showed that urban residence (OR = 2.356, 95%CI: 1.251-4.435) and post-term birth (OR = 3.275, 95%CI: 1.396-7.683) were significantly associated with SRBD. Preterm birth (OR = 2.255, 95%CI: 1.021-4.980) and parental education level of university and above (OR = 0.265, 95%CI: 0.083-0.850) were significantly associated with snoring. Overweight (OR = 2.063, 95%CI: 1.062-4.006) was also related to snoring. Conclusions : The prevalences of SRBD and snoring were similar to those reported in previous studies. Urban residence and post-term birth were important influencing factors for SRBD; overweight, highest parental education level (university and above) and preterm birth were key factors affecting snoring in adolescents.