Effects of different over-fired drying methods on the aroma of Lu'an Guapian tea.

Over-fired drying, a crucial process in the production of Lu'an Guapian (LAGP) tea, greatly enriches the tea's aroma. In this study, the aroma compounds of LAGP tea processed through pulley charcoal drying (PCD), roller drying (RD), roller-conveyor drying (RCD), and hot air drying (HD) were analyzed using gas chromatography-mass spectrometry. A subsequent analysis of aroma extraction dilution analysis and odor activity values revealed that (E)-ß-ionone, dimethyl sulfide, (E,E)-2,4-heptadienal, geraniol, linalool, benzeneacetaldehyde, coumarin, 2-ethyl-3,5-dimethyl-pyrazine, indole, hexanal, (Z)-jasmone, and (Z)-3-hexen-1-ol were the key contributors to the samples' aroma variation. Moreover, a quantitative descriptive analysis and aroma recombination and omission experiments analysis revealed that (E)-ß-ionone is the most critical contributor to the formation of floral aroma in tea processed using PCD, whereas (E,E)-2,4-heptadienal is responsible for the more pronounced fresh aroma in tea processed using HD. In addition, 2-ethyl-3,5-dimethyl-pyrazine contributes to the formation of a roasted aroma in tea processed using RD and RCD. The study results provide a theoretical basis for choosing the processing method, especially for drying, to obtain high-quality LAGP tea.

Metabolomics analysis reveals the mechanism underlying the improvement in the color and taste of yellow tea after optimized yellowing.

In this study, an optimized yellowing process for yellow tea (YT) was developed by response surface methodology. The results showed that increasing the yellowing temperature from 20 °C to 34 °C, increasing the relative humidity from 55% to 67%, and reducing the yellowing time from 48 h to 16 h, caused a 40.5% and 43.2% increase in the yellowness and sweetness of YT, respectively, and improved the consumer acceptability by 36.8%. Moreover, metabolomics was used to explore the involved mechanisms that resulted in the improved YT quality. The optimized yellowing promoted the hydrolysis of 5 gallated catechins, 6 flavonoid glycosides, theogallin and digalloylglucose, resulting in the accumulation of 5 soluble sugars and gallic acid. Meanwhile, it promoted the oxidative polymerization of catechins (e.g., theaflagallin, δ-type dehydrodicatechin and theasinensin A), but decelerated the degradation of chlorophylls. Overall, this optimized yellowing process could serve as a guide to a shorter yellowing cycle.

Formation of aroma characteristics driven by volatile components during long-term storage of An tea.

The aim of this study was to reveal the molecular basis of aroma changes during storage of An tea (AT). The key volatile compounds in AT were screened using SPME-GC-MS and SPE-GC-MS analytical techniques in combination with odor activity value (OAV) and flavor dilution factor (FD). The results showed that with the increase of storage time the stale and woody aromas were revealed. Esters, acids and hydrocarbons are the main types of volatile compounds in AT, and their content accounts for 52.69 %-61.29 % of the total volatile compounds. The key volatile compounds with stale and woody aromas during AT storage were obtained by OAV value and FD value, namely ketoisophorone (flavor dilution factor, FD = 64), linalool oxide C (FD = 64), 1-octen-3-ol (OAV > 1, FD = 32), 1,2-dimethoxybenzene (FD = 16), naphthalene (OAV > 1, FD = 32), 3,4-dimethoxytoluene (FD = 16), and 1,2,3-trimethoxybenzene (FD = 8). Our research provides a scientific basis and insights for the improvement of quality during the storage of dark tea.

Monitoring green tea fixation quality by intelligent sensors: comparison of image and spectral information.

BACKGROUND: Intelligent monitoring of fixation quality is a prerequisite for automated green tea processing. To meet the requirements of intelligent monitoring of fixation quality in large-scale production, fast and non-destructive detection means are urgently needed. Here, smartphone-coupled micro near-infrared spectroscopy and a self-built computer vision system were used to perform rapid detection of the fixation quality in green tea processing lines. RESULTS: Spectral and image information from green tea samples with different fixation degrees were collected at-line by two intelligent monitoring sensors. Competitive adaptive reweighted sampling and correlation analysis were employed to select feature variables from spectral and color information as the target data for modeling, respectively. The developed least squares support vector machine (LS-SVM) model by spectral information and the LS-SVM model by image information achieved the best discriminations of sample fixation degree, with both prediction set accuracies of 100%. Compared to the spectral information, the image information-based support vector regression model performed better in moisture prediction, with a correlation coefficient of prediction of 0.9884 and residual predictive deviation of 6.46. CONCLUSION: The present study provided a rapid and low-cost means of monitoring fixation quality, and also provided theoretical support and technical guidance for the automation of the green tea fixation process. © 2022 Society of Chemical Industry.

Rapid detection of multiple colorant adulteration in Keemun black tea based on hemp spherical AgNPs-SERS.

This study synthesized stable and sensitive hemp spherical AgNPs as the SERS substrate for the simultaneous and rapid detection of sunset yellow, lemon yellow, carmine and erythrosine adulteration in black tea. With R6G as the probe molecule, the AgNPs were determined to have satisfactory stability over 60 days with an enhancement factor of 108. The effects of three variable screening methods on model performance were compared. Among them, CARS-PLS exhibited superior performance for the quantification of all the four colorants, with prediction set correlation coefficients of 0.95, 0.97, 0.99 and 0.88, respectively. The differentiation of the mixed colorants was also achieved, with recoveries ranging from 91.87 % to 106.5 % with RSD value <1.97 %, demonstrating the high accuracy and precision of the proposed method. The results indicate that AgNPs-based SERS is an effective method and has substantial potential for application in the identification and quantification of colorant in tea.

Evaluation of the effects of solar withering on nonvolatile compounds in white tea through metabolomics and transcriptomics.

The mechanism through which solar withering (SW) affects the quality of white tea is unclear. To address this gap in the literature, in this study, we used metabolomics and transcriptomics to investigate the effect of SW on the quality of WT. WT that underwent SW was slightly more bitter and astringent than WT that underwent natural withering (control group). Specifically, SW considerably increased the concentration of astringent flavonoids and flavone glycosides in WT. This increase was mainly attributed to the upregulated expression of key genes in the shikimic acid, phenylpropanoid, and flavonoid biosynthesis pathways, such as shikimate kinase, chalcone synthase, and flavonol synthase. In addition, SW experienced considerable heat and light stress. The levels of glycerophosphatidylcholine and carbohydrates increased in response to the stress, which also affected the taste of WT. The results of this study indicate the mechanism through which SW affects the quality of WT.

Effects of Microbial Action and Moist-Heat Action on the Nonvolatile Components of Pu-Erh Tea, as Revealed by Metabolomics.

Microbial action and moist-heat action are crucial factors that influence the piling fermentation (PF) of Pu-erh tea. However, their effects on the quality of Pu-erh tea remain unclear. In this study, the effects of spontaneous PF (SPPF) and sterile PF (STPF) on the chemical profile of Pu-erh tea were investigated for the first time, and sun-dried green tea was used as a raw material to determine the factors contributing to the unique quality of Pu-erh tea. The results indicated that the SPPF-processed samples had a stale and mellow taste, whereas the STPF-processed samples had a sweet and mellow taste. Through metabolomics-based analysis, 21 potential markers of microbial action (including kaempferol, quercetin, and dulcitol) and 10 potential markers of moist-heat action (including ellagic acid, ß-glucogallin, and ascorbic acid) were screened among 186 differential metabolites. Correlation analysis with taste revealed that metabolites upregulated by moist-heat and microbial action were the main factors contributing to the staler mellow taste of the SPPF-processed samples and the sweeter mellow taste of the STPF-processed samples. Kaempferol, quercetin, and ellagic acid were the main active substances formed under microbial action. This study provides new knowledge regarding the quality formation mechanism of Pu-erh tea.

Improving the flavor of summer green tea (Camellia sinensis L.) using the yellowing process.

Summer green tea (SGT) has poor flavor due to its high levels of bitterness and astringency. The present study aimed to improve the flavor of SGT using the yellowing process. The results showed that after the yellowing process, the sweetness and overall acceptability increased, and the content of gallated catechins and flavonol glycosides decreased by 30.2% and 27.4%, respectively, as did the bitterness and astringency of SGT. Yellowing caused a decrease in the concentration of some aroma compounds, such as (z)-3-hexen-1-ol, 1-hexanol, pentanal, heptanal and 1-octanol, which caused grassy, floral and fruity aromas. In contrast, the concentrations of 1-octen-3-ol, benzene acetaldehyde and ß-ionone increased, which have mushroom and sweet aromas. Meanwhile, the sweetness and umami of SGT were enhanced by the addition of selected aroma compounds (1-octen-3-ol, benzene acetaldehyde and ß-ionone), demonstrating that the yellowing process improves the flavor of SGT through odor-taste interactions.

Quality chemical analysis of crush-tear-curl (CTC) black tea from different geographical regions based on UHPLC-Orbitrap-MS.

Crush-tear-curl (CTC) black tea is a popular beverage, owing to its unique taste characteristics and health benefits. However, differences in the taste quality and chemical profiles of CTC black tea from different geographical regions remain unclear. In this study, 28 CTC black tea samples were collected from six geographical regions and analyzed using electronic tongue and ultrahigh performance liquid chromatography-Orbitrap-mass spectroscopy. The e-tongue analysis indicated that each region's CTC black tea has its own relatively prominent taste characteristics: Sri Lanka (more umami and astringent), North India (more umami), China (more sweetness and astringent), South India (moderate umami and sweetness), and Kenya (moderate umami and astringent). Based on multivariate statistical analysis, 78 metabolites were tentatively identified and used as potential markers for CTC black tea of different origins, mainly including amino acids, flavone/flavonol glycosides, and pigments. Different metabolites, which contributed to the taste characteristics of CTC black tea, were clarified by partial least squares regression correlation analysis. Our findings may serve as useful references for future studies on origin traceability and quality characteristic determination of CTC black teas. PRACTICAL APPLICATION: This study provides useful references for future studies on the origin traceability and taste characteristic determination of CTC black teas from different geographical regions.

Rapid and real-time detection of black tea fermentation quality by using an inexpensive data fusion system.

Intelligent identification of black tea fermentation quality is becoming a bottleneck to industrial automation. This study presents at-line rapid detection of black tea fermentation quality at industrial scale based on low-cost micro-near-infrared spectroscopy (NIRS) and laboratory-made computer vision system (CVS). High-performance liquid chromatography and a spectrophotometer were used for determining the content of catechins and theaflavins, and the color of tea samples, respectively. Hierarchical cluster analysis combined with sensory evaluation was used to group samples through different fermentation degrees. A principal component analysis-support vector machine (SVM) model was developed to discriminate the black tea fermentation degree using color, spectral, and data fusion information; high accuracy (calibration = 95.89%, prediction = 89.19%) was achieved using mid-level data fusion. In addition, SVM model for theaflavins content prediction was established. The results indicated that the micro-NIRS combined with CVS proved a portable and low-cost tool for evaluating the black tea fermentation quality.

Micro-NIR spectrometer for quality assessment of tea: Comparison of local and global models.

Near-infrared (NIR) spectroscopy is an effective tool for analyzing components relevant to tea quality, especially catechins and caffeine. In this study, we predicted catechins and caffeine content in green and black tea, the main consumed tea types worldwide, by using a micro-NIR spectrometer connected to a smartphone. Local models were established separately for green and black tea samples, and these samples were combined to create global models. Different spectral preprocessing methods were combined with linear partial-least squares regression and nonlinear support vector machine regression (SVR) to obtain accurate models. Standard normal variate (SNV)-based SNV-SVR models exhibited accurate predictive performance for both catechins and caffeine. For the prediction of quality components of tea, the global models obtained results comparable to those of the local models. The optimal global models for catechins and caffeine were SNV-SVR and particle swarm optimization (PSO)-simplified SNV-PSO-SVR, which achieved the best predictive performance with correlation coefficients in prediction (Rp) of 0.98 and 0.93, root mean square errors in prediction of 9.83 and 2.71, and residual predictive deviations of 4.44 and 2.60, respectively. Therefore, the proposed low-price, compact, and portable micro-NIR spectrometer connected to smartphones is an effective tool for analyzing tea quality.

Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics.

BACKGROUND: Rapid and accurate diagnosis of nitrogen (N) status in field crops is of great significance for site-specific N fertilizer management. This study aimed to evaluate the potential of hyperspectral imaging coupled with chemometrics for the qualitative and quantitative diagnosis of N status in tea plants under field conditions. RESULTS: Hyperspectral data from mature leaves of tea plants with different N application rates were preprocessed by standard normal variate (SNV). Partial least squares discriminative analysis (PLS-DA) and least squares-support vector machines (LS-SVM) were used for the classification of different N status. Furthermore, partial least squares regression (PLSR) was used for the prediction of N content. The results showed that the LS-SVM model yielded better performance with correct classification rates of 82% and 92% in prediction sets for the diagnosis of different N application rates and N status, respectively. The PLSR model for leaf N content (LNC) showed excellent performance, with correlation coefficients of 0.924, root mean square error of 0.209, and residual predictive deviation of 2.686 in the prediction set. In addition, the important wavebands of the PLSR model were interpreted based on regression coefficients. CONCLUSION: Overall, our results suggest that the hyperspectral imaging technique can be an effective and accurate tool for qualitative and quantitative diagnosis of N status in tea plants. © 2019 Society of Chemical Industry.

Geographical origin traceability of Keemun black tea based on its non-volatile composition combined with chemometrics.

BACKGROUND: Non-volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non-volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non-volatile compounds. RESULTS: Seventy-two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high-performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%-99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)-catechin, (-)-epigallocatechin gallate, theaflavin-3-gallate, theaflavin-3,3'-di-gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross-validation rate (88.9%) for KBT samples. CONCLUSION: The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry.