Predictive models for sensory score and physicochemical composition of Yuezhou Longjing tea using near-infrared spectroscopy and data fusion.

In this study, NIR quantitative prediction model was established for sensory score and physicochemical components of different varieties and quality grades of Yuezhou Longjing tea. Firstly, L, a, b color factors and diffuse reflection spectral data are collected for each sample. Subsequently, the original spectrum is preprocessed. Three techniques for selecting variables, CARS, BOSS, and SPA, were utilized to extract optimal feature bands. Finally, the spectral data extracted from feature bands were fused with L, a and b color factors to build SVR and PLSR prediction models. enabling the rapid non-destructive discrimination of different varieties and grades of Yuezhou Longjing tea. The outcomes demonstrated that BOSS was the best variable selection technique for sensory score and the distinctive caffeine wavelengths, CARS, however, was the best variable selection technique for catechins distinctive wavelengths. Additionally, the middle-level data fusion-based non-linear prediction models greatly outperformed the linear prediction models. For the prediction models of sensory score, catechins, and caffeine, the relative percent deviation (RPD) values were 2.8, 1.6, and 2.6, respectively, suggesting the good predictive ability of the models. In conclusion, evaluating the quality of the five Yuezhou Longjing tea varieties using near-infrared spectroscopy and data fusion have proved as feasible.

NIR Spectral Inversion of Soil Physicochemical Properties in Tea Plantations under Different Particle Size States.

Soil fertility is vital for the growth of tea plants. The physicochemical properties of soil play a key role in the evaluation of soil fertility. Thus, realizing the rapid and accurate detection of soil physicochemical properties is of great significance for promoting the development of precision agriculture in tea plantations. In recent years, spectral data have become an important tool for the non-destructive testing of soil physicochemical properties. In this study, a support vector regression (SVR) model was constructed to model the hydrolyzed nitrogen, available potassium, and effective phosphorus in tea plantation soils of different grain sizes. Then, the successful projections algorithm (SPA) and least-angle regression (LAR) and bootstrapping soft shrinkage (BOSS) variable importance screening methods were used to optimize the variables in the soil physicochemical properties. The findings demonstrated that soil particle sizes of 0.25-0.5 mm produced the best predictions for all three physicochemical properties. After further using the dimensionality reduction approach, the LAR algorithm (R2C = 0.979, R2P = 0.976, RPD = 6.613) performed optimally in the prediction model for hydrolytic nitrogen at a soil particle size of 0.25~0.5. The models using data dimensionality reduction and those that used the BOSS method to estimate available potassium (R2C = 0.977, R2P = 0.981, RPD = 7.222) and effective phosphorus (R2C = 0.969, R2P = 0.964, RPD = 5.163) had the best accuracy. In order to offer a reference for the accurate detection of soil physicochemical properties in tea plantations, this study investigated the modeling effect of each physicochemical property under various soil particle sizes and integrated the regression model with various downscaling strategies.

Detection of volatile organic compounds in adulterated tea using Fourier transform infrared spectroscopy and Proton-transfer-reaction mass spectrometry.

Aroma is a key factor used to evaluate tea quality. Illegal traders usually add essence to expired or substandard tea to improve its aroma so as to gain more profit. Traditional physical and chemical testing methods are time-consuming and costly. Furthermore, rapid detection techniques, such as near-infrared spectroscopy and machine vision, can only be used to detect adulterated powdered solid essences in tea. In this study, proton-transfer reaction mass spectrometry (PTR-MS) and Fourier-transform infrared spectroscopy (FTIR) were employed to detect volatile organic compounds (VOCs) in samples, and rapid detection of different tea adulterated liquid essence was achieved. The prediction accuracies of PTR-MS and FTIR reached over 0.941 and 0.957, respectively, and the minimum detection limits were lower than the actual used values in both. In this study, the different application scenarios of the two technologies are discussed based on their performance characteristics.

Moisture content monitoring in withering leaves during black tea processing based on electronic eye and near infrared spectroscopy.

Monitoring the moisture content of withering leaves in black tea manufacturing remains a difficult task because the external and internal information of withering leaves cannot be simultaneously obtained. In this study, the spectral data and the color/texture information of withering leaves were obtained using near infrared spectroscopy (NIRS) and electronic eye (E-eye), respectively, and then fused to predict the moisture content. Subsequently, the low- and middle-level fusion strategy combined with support vector regression (SVR) was applied to detect the moisture level of withering leaves. In the middle-level fusion strategy, the principal component analysis (PCA) and random frog (RF) were employed to compress the variables and select effective information, respectively. The middle-level-RF (cutoff line = 0.8) displayed the best performance because this model used fewer variables and still achieved a satisfactory result, with 0.9883 and 5.5596 for the correlation coefficient of the prediction set (Rp) and relative percent deviation (RPD), respectively. Hence, our study demonstrated that the proposed data fusion strategy could accurately predict the moisture content during the withering process.

Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology.

Moisture content is an important indicator that affects green tea processing. In this study, taking Chuyeqi tea as the research object, a quantitative prediction model of the changes in moisture content during the processing of green tea was constructed based on machine vision and near-infrared spectroscopy technology. First, collect the spectrum and image information in the process of spreading, fixation, first-drying, carding, and second-drying. The competitive adaptive reweighted sampling (CARS) method is then used to extract the characteristic wavelengths in the spectrum, and the image's 9 color features and 6 texture features are combined to establish linear PLSR and nonlinear SVR prediction models by fusing the data information from the two sensors. The results show that, when compared to single data, the PLSR and SVR models based on low-level data fusion do not effectively improve the model's prediction accuracy, but rather produce poor prediction results. In contrast, the PLSR and SVR models established by middle-level data fusion have improved the prediction accuracy of moisture content in green tea processing. Among them, the established SVR model has the best effect. The correlation coefficient of the calibration set (Rc) and the root mean square error of calibration (RMSEC) are 0.9804 and 0.0425, respectively, the correlation coefficient of the prediction set (Rp) and the root mean square error of prediction (RMSEP) are 0.9777 and 0.0490 respectively, and the relative percent deviation is 4.5002. The results show that the middle data fusion based on machine vision and near-infrared spectroscopy technology can effectively predict the moisture content in the processing of green tea, which has important guiding significance for overcoming the low prediction accuracy of a single sensor.

Black tea aroma formation during the fermentation period.

The present study aimed to systematically investigate black tea aroma formation during the fermentation period. In total, 158 volatile compounds were identified. Of these, most amino acid-derived volatiles (AADVs) and carotenoid-derived volatiles (CDVs) showed significant increases, while fatty acid-derived volatiles (FADVs) and volatile terpenoids (VTs) displayed diverse changes during the fermentation period. During this time, fatty acids, amino acids, carotenoids, and glycosidically bound volatiles (GBVs, especially primeverosides) were found to degrade to form aroma components. Further, equivalent quantification of aroma showed that the intensity of green scent was notably decreased, while the intensities of sweet and floral/fruity scents were greatly increased and gradually dominated the aroma of tea leaves. AADVs and CDVs were shown to make greater contributions to the formation of sweet and floral/fruity scents than VTs. Our study provides a detailed characterization of the formation of sweet and floral/fruity aromas in black tea during the fermentation period.

Robustness and accuracy evaluation of moisture prediction model for black tea withering process using hyperspectral imaging.

The rapid and non-destructive detection of moisture in withering leaves is an unsolved problem because the leaves are stacked together and have random orientation. To address this issue, this study aimed to establish more robust and accurate models. The performance of front side, back side and multi-region models were compared, and the front side model showed the worst transferability. Therefore, five effective wavelength (EW) selection algorithms were combined with a successive projection algorithm (SPA) to select EWs. It was found that the shuffled frog leaping algorithm (SFLA) combined with SPA was the best method for the front side model for moisture analyses. Based on the selected EWs, the extreme learning machine (ELM) became the model with the best self-verification result. Subsequently, moisture distribution maps of withering leaves were successfully generated. Considering the processing demand of withering leaves, local region models developed based on partial least squares and the SFLA-SPA method were applied to predict the moisture of withering leaves in the local and stacked region. The results showed that the RPD, Rcv and Rp values were above 1.6, 0.870 and 0.897, respectively. These results provide a useful reference for the non-destructive detection of moisture in withering leaves.

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging.

Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.

Rapid characterization of the volatile profiles in Pu-erh tea by gas phase electronic nose and microchamber/thermal extractor combined with TD-GC-MS.

Aroma plays an important role in the quality of Pu-erh tea. However, the quality evaluation of Pu-erh tea aroma is heavily relied on the experience of sensory evaluation, and the theoretical research is relatively scarce. In the present work, the volatile compounds in Pu-erh tea were characterized by using gas phase electronic nose (e-nose) and microchamber/thermal extractor (µ-CTE) combined with thermal desorption coupled to gas chromatography-mass spectrometry (TD-GC-MS). A satisfactory discrimination model (R2 Y = 0.95, Q2  = 0.807) was obtained by using orthogonal partial least squares discriminant analysis (OPLS-DA) based on the odor fingerprint of different brands of Pu-erh tea. In addition, based on the double criterion of multivariate analysis with VIP >1.0 and univariate analysis with p ≤ 0.001, 39 volatile components were identified to contribute greatly to the discrimination of five brands of Pu-erh tea. The results suggested that gas phase e-nose and µ-CTE combined with TD-GC/MS were simple, rapid techniques to characterize the volatile compounds in Pu-erh tea and were allowed to effectively distinguish different brands of Pu-erh tea, which would provide an important reference on the quality assessment of Pu-erh tea. PRACTICAL APPLICATION: This work demonstrates that the volatile compounds in Pu-erh tea are simply and rapidly characterized by using µ-CTE/TD-GC/MS and gas phase e-nose, allowing to effectively distinguish different brands of Pu-erh tea, which can provide an important reference for the quality assessment and authentication of Pu-erh tea.

Investigation on green tea lipids and their metabolic variations during manufacturing by nontargeted lipidomics.

Lipids are hydrophobic metabolites implicated in tea flavor quality. Understanding their transformations during tea manufacture is of particular interest. To date, the detailed lipid composition and variations during green tea manufacture are largely unknown. Herein, we performed a comprehensive characterization of the dynamic changes of lipids during green tea manufacture, by applying nontargeted lipidomics using ultrahigh performance liquid chromatography-quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Exactive/MS) combined with chemometric tools. Totally, 283 lipid species were detected, covering 20 subclasses. Significant lipidomic variations were observed during green tea manufacture, especially in the fixation stage, mainly associated with chlorophyll decomposition, phosphatidic acids (PAs) reduction and glycolipids degradation, which potentially contribute to tea color and aroma quality. Specifically, the most prominent decrease of PAs content during green tea manufacture was identified for the first time. This study provides insights into the lipid metabolic fates upon green tea manufacture, and their roles in green tea sensory quality.

Aroma dynamic characteristics during the process of variable-temperature final firing of Congou black tea by electronic nose and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry.

The drying technology is crucial to the quality of Congou black tea. In this study, the aroma dynamic characteristics during the variable-temperature final firing of Congou black tea was investigated by electronic nose (e-nose) and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). Varying drying temperatures and time obtained distinctly different types of aroma characteristics such as faint scent, floral aroma, and sweet fragrance. GC × GC-TOFMS identified a total of 243 volatile compounds. Clear discrimination among different variable-temperature final firing samples was achieved by using partial least squares discriminant analysis (R2Y = 0.95, Q2 = 0.727). Based on a dual criterion of variable importance in the projection value (VIP > 1.0) and one-way ANOVA (p < 0.05), ninety-one specific volatile biomarkers were identified, including 2,6-dimethyl-2,6-octadiene and 2,5-diethylpyrazine with VIP > 1.5. In addition, for the overall odor perception, e-nose was able to distinguish the subtle difference during the variable-temperature final firing process.

Quantitation of pyrazines in roasted green tea by infrared-assisted extraction coupled to headspace solid-phase microextraction in combination with GC-QqQ-MS/MS.

Pyrazines play an important role in the characteristic flavor of roasted green tea due to powerful strong odours and low sensory thresholds. It is important to analyze these compounds reliably and rapidly in roasted green tea. In this study, infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) and gas chromatography-triple quadrupole-tandem mass spectrometry (GC-QqQ-MS/MS) were developed and validated to determine the pyrazines in roasted green tea. Good linear correlation coefficients (0.9955-0.9996) were obtained over the concentration ranges of 10-5000 ng/mL. The limits of detection (LODs) and limits of quantification (LOQs) for the pyrazines were in the range of 1.46-3.27 ng/mL and 4.89-10.90 ng/mL, respectively. The average recoveries varied from 84% to 119%. The method was used to analyze the pyrazines in roasted green tea manufactured by different final firing methods, the results revealed that microwave final firing method had maximum contents of pyrazines, and significantly improved the aroma quality. In addition, there were great disparities of pyrazines in flatten-shaped green tea and strip-shaped green tea according to the appearance. The result is expected to better understand the role of pyrazines related to aroma quality of roasted green tea and improve processing technology.

Effects of withering on the main physical properties of withered tea leaves and the sensory quality of congou black tea.

To explore the relationship between the moisture content of withered tea leaves and their physical properties (i.e., elasticity, plasticity, flexibility, and texture) during withering, texture analyzer was employed to test the elasticity and flexibility of withered tea leaves with different moisture contents. The texture was evaluated by computer vision technology. The withered tea leaves with different moisture contents were used to process congou black tea, which was then subjected to sensory evaluation. Results showed that good elasticity, optimal flexibility, and plasticity were achieved when the moisture content of the withered tea leaves of Fudingdabai comprising two leaves and one bud varied arranging from 65.51 to 61.48%. The sensory evaluation of congou black tea revealed that moderate withering was better than long-term withering and that both moderate and long-term withering were better than no withering during processing. The moisture content was significantly correlated with the flexibility and plasticity of the withered tea leaves. Fresh tea leaves undergoing moderate withering with moisture content of 65.51-61.48% to process congou black tea, good tea shape and liquor color were achieved. This study provided new evidence that the moisture content of withered tea leaves significantly affected the quality of black tea.

Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis.

Aroma assessment remains difficult and uncertain in the present sensory assessment system. It is highly desirable to develop a new assessment method to discriminate the quality of various teas in the tea market. In the present work, based on linear discriminant analysis and principal component analysis, the aroma of dry and wet samples of different Xi-hu Longjing and Pu-erh teas were tested and differentiated by electronic noses (e-nose). The results confirm that e-nose can discriminate different priced Xi-hu Longjing tea samples in the range of 80-800 RMB/500 g and varying storage years of Pu-erh tea samples. Furthermore, for the detection of both dry and wet samples of Longjing and Pu-erh teas, the results reveal that all samples have specific aroma characteristics that e-nose can recognize. More importantly, contribution analysis in sensors indicates that nitrogen oxides, methane and alcohols are the characteristic components that contribute to the fragrances of different priced Xi-hu Longjing teas, while nitrogen oxides, aromatic benzene and amines make the fragrances of Pu-erh teas with different storage years disparate. PRACTICAL APPLICATION: This work demonstrates that e-nose can rapidly distinguish tea products with different price levels and varying storage years. With the advantages of ease of use, high portability and flexibility, e-nose will be widely expanded and applied in refined processing and the development of flavored foods.

Rutin, γ-Aminobutyric Acid, Gallic Acid, and Caffeine Negatively Affect the Sweet-Mellow Taste of Congou Black Tea Infusions.

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.

Rapid determination by near infrared spectroscopy of theaflavins-to-thearubigins ratio during Congou black tea fermentation process.

The theaflavin-to-thearubigin ratio (TF/TR) is an important parameter for evaluating the degree of fermentation and quality characteristics of Congou black tea. Near infrared (NIR) spectroscopy, one of the most promising techniques for evaluating large-scale tea processing quality, in association with chemometrics, can be used as a selection tool when a fast determination of the requested parameters is required. The aim of this work is to develop a unique model for the determination of TF/TR. First, 11 key wavelength variables were screened by synergy interval partial least-squares regression (SI-PLS) and competitive adaptive reweighted sampling (CARS). Based on these characteristic variables, a new extreme learning machine (ELM) combined with an adaptive boosting (ADABOOST) algorithm (ELM-ADABOOST) was applied to construct the nonlinear prediction model for TF/TR, and an independent external set was used for the validation. A determinate coefficient (Rp2) of 0.893, root mean square error of prediction (RMSEP) of 0.0044, RSD below 10%, and RPD above 3 were acquired in the prediction model. These results demonstrate that NIR can be used to rapidly determine the TF/TR value during fermentation, and it effectively simplify the model and improve the prediction accuracy when combined with the SI-CARS variable.

Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method.

Withering is the first step in the processing of congou black tea. With respect to the deficiency of traditional water content detection methods, a machine vision based NDT (Non Destructive Testing) method was established to detect the moisture content of withered leaves. First, according to the time sequences using computer visual system collected visible light images of tea leaf surfaces, and color and texture characteristics are extracted through the spatial changes of colors. Then quantitative prediction models for moisture content detection of withered tea leaves was established through linear PLS (Partial Least Squares) and non-linear SVM (Support Vector Machine). The results showed correlation coefficients higher than 0.8 between the water contents and green component mean value (G), lightness component mean value (L*) and uniformity (U), which means that the extracted characteristics have great potential to predict the water contents. The performance parameters as correlation coefficient of prediction set (Rp), root-mean-square error of prediction (RMSEP), and relative standard deviation (RPD) of the SVM prediction model are 0.9314, 0.0411 and 1.8004, respectively. The non-linear modeling method can better describe the quantitative analytical relations between the image and water content. With superior generalization and robustness, the method would provide a new train of thought and theoretical basis for the online water content monitoring technology of automated production of black tea.

Far-Infrared Optimization of the Fragrance-Improving Process with Temperature and Humidity Control for Green Tea.

In this study, a Box-Behnken design was used to explore the effect of a new technology on green tea fragrance improvement and to optimize fragrance-improving with a bilayer far-infrared fragrance-improving machine with temperature and humidity control. Based on the results of previous single-factor experiments, the main biochemical composition and sensory evaluation scores of the fragrance-improved samples were used as investigation indices. The new fragrance-improving technology was compared with the traditional far-infrared fragrance-improving process, roller pot fragrance improvement, and hot air rotary fragrance improvement. The results show that the optimal parameter combination of the new technology consists of a temperature of 128.00 °C, relative humidity of 70.00 g/h, and transmission speed of 435.00 r/min. With these process parameters, the amino acids, tea polyphenols, flavonoids, soluble sugar, catechins, and caffeine in the fragrance-improved samples reached 3.86%, 32.29%, 5.59%, 4.45%, 8.97%, and 2.75%, respectively. The quality material weight value was 11.72%. The shape, color, taste, and aroma of the fragrance-improved samples made using these parameters were found to be best, with a sensory quality score of 87.40, which is significantly higher than that of other fragrance-improving methods. The energy consumption was 0.19 RMB/kg, which was reduced by more than 50% compared with the other methods, and the production efficiency was more than 30% higher than the traditional methods. This new far-infrared fragrance-improving technology overcomes the yellowish and grayish color of fragrance-improved tea samples that is caused by the traditional fragrance-improving approach, and will provide technical guidance for actual green tea production. PRACTICAL APPLICATION: Our proposed approach innovatively integrates humidity and rotational speed as factors for fragrance improvement in Chinese tea process. The findings of this work provide new technical for fragrance improvement processes.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

Comprehensive Lipidome-Wide Profiling Reveals Dynamic Changes of Tea Lipids during Manufacturing Process of Black Tea.

As important biomolecules in Camellia sinensis L., lipids undergo substantial changes during black tea manufacture, which is considered to contribute to tea sensory quality. However, limited by analytical capacity, detailed lipid composition and its dynamic changes during black tea manufacture remain unclear. Herein, we performed tea lipidome profiling using high resolution liquid chromatography coupled to mass spectrometry (LC-MS), which allows simultaneous and robust analysis of 192 individual lipid species in black tea, covering 17 (sub)classes. Furthermore, dynamic changes of tea lipids during black tea manufacture were investigated. Significant alterations of lipid pattern were revealed, involved with chlorophyll degradation, metabolic pathways of glycoglycerolipids, and other extraplastidial membrane lipids. To our knowledge, this report presented most comprehensive coverage of lipid species in black tea. This study provides a global and in-depth metabolic map of tea lipidome during black tea manufacture.