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Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network's capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study's proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan's tea gardens. [ABSTRACT FROM AUTHOR]

Aroma plays a pivotal role in defining tea quality and distinctiveness, and tea producers have often observed that specific drought conditions are closely associated with the formation and accumulation of characteristic aroma compounds in tea leaves. However, there is still limited understanding of the differential strategies employed by various tea cultivars in response to drought stress for the accumulation of key volatile aroma compounds in fresh tea leaves, as well as the associated metabolic pathways involved in aroma formation. In this study, two widely cultivated tea cultivars in China, Fuding Dabai (FD) and Wuniuzao (WNZ), were examined to assess the impact of mild field drought stress on the composition and accumulation of key volatile aroma compounds in fresh leaves using headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) and headspace solid phase micro-extraction gas chromatography–mass spectrometry (HS-SPME-GC-MS) technologies. Results revealed that drought stress led to a substantial increase in the diversity of volatile compounds (VOCs) in FD, while WNZ exhibited a notable rise in low-threshold VOC concentrations, amplifying sweet, floral, fruity, and earthy aroma profiles in post-drought fresh leaves. Through partial least squares discriminant analysis (PLS-DA) of HS-GC-IMS and HS-SPME-GC-MS data, integrating variable importance projection (VIP) scores and odor activity values (OAVs) above 1, 9, and 13, key odor-active compounds were identified as potential markers distinguishing the drought responses in the two cultivars. These compounds serve as crucial indicators of the aromatic profile shifts induced by drought, providing insights into the differential metabolic strategies of the cultivars. Additionally, KEGG enrichment analysis revealed 12 metabolic pathways, such as terpenoid biosynthesis, fatty acid synthesis, cutin, suberine, and wax biosynthesis, and phenylalanine metabolism, which may play crucial roles in the formation and accumulation of VOCs in tea leaves under drought stress. These findings provide a comprehensive framework for understanding the cultivar-specific mechanisms of aroma formation and accumulation in tea leaves under mild drought conditions. [ABSTRACT FROM AUTHOR]

Abstract Tea (Camellia sinensis (L.) O. Kuntze) is a popular beverage consumed worldwide. To establish fundamental scientific data, we analyzed the amino acids and volatile compounds in seven tea cultivars grown in Korea investigated phenotype also. Phenotypically, the leaf area and greenness index of young shoots and leaf blades were particularly different among the four Korean cultivars. Nine amino acids were detected from each cultivar, with the total amino acid and theanine contents being 9.08–41.42 and 2.81–24.60 mg/g, respectively. Moreover, 107 volatile compounds were identified as common components among tea cultivars using headspace solid-phase microextraction / gas chromatography–mass spectrometry (HS-SPME/GC-MS), and 38 key compounds were identified using partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA). The (Z)-linalool oxide (furanoid) concentrations were significantly high in Korean tea plant cultivars, and linalool concentrations were also high or low, but had high relative contents. Linalool and its various oxides are the major compounds responsible for the tea aroma. In conclusion, Korean tea cultivars have distinct characteristics, and the results of this study will form the basis for identifying Korean tea plant cultivars that can produce high-value tea products.

Tea (Camellia sinensis (L.) O. Kuntze) is a popular beverage consumed worldwide. To establish fundamental scientific data, we analyzed the amino acids and volatile compounds in seven tea cultivars grown in Korea investigated phenotype also. Phenotypically, the leaf area and greenness index of young shoots and leaf blades were particularly different among the four Korean cultivars. Nine amino acids were detected from each cultivar, with the total amino acid and theanine contents being 9.08–41.42 and 2.81–24.60 mg/g, respectively. Moreover, 107 volatile compounds were identified as common components among tea cultivars using headspace solid-phase microextraction / gas chromatography–mass spectrometry (HS-SPME/GC-MS), and 38 key compounds were identified using partial least squares-discriminant analysis (PLS-DA) and hierarchical cluster analysis (HCA). The (Z)-linalool oxide (furanoid) concentrations were significantly high in Korean tea plant cultivars, and linalool concentrations were also high or low, but had high relative contents. Linalool and its various oxides are the major compounds responsible for the tea aroma. In conclusion, Korean tea cultivars have distinct characteristics, and the results of this study will form the basis for identifying Korean tea plant cultivars that can produce high-value tea products. [ABSTRACT FROM AUTHOR]

Reasonable application of nitrogen fertilizer can improve the yield and quality of tea. This study used Jin Xuan as the tested variety and applied nitrogen fertilizer at rates of 0 kg/ha (N0), 150 kg/ha (N150), 300 kg/ha (N300), and 450 kg/ha (N450) in the summer and autumn seasons to analyze the effects of nitrogen application on the quality components and gene expression of tea leaves. The results showed that the N150 treatment significantly increased total polyphenols (TP), total catechins (TC), and caffeine contents, with the most significant increase observed in the content of six monomers of catechins (EGCG, ECG, EGC, GCG, GC, and EC) in the summer. The N300 treatment significantly increased TP and AA contents in the autumn while decreasing TC content. Additionally, the N300 treatment significantly increased caffeine and theanine contents in the autumn. Notably, the N300 treatment significantly increased both summer and autumn tea yields. Multivariate statistical analysis showed that TPs, AAs, TCs, EGC, and caffeine were key factors affecting the quality of Jin Xuan. Furthermore, the N150 treatment upregulated the expression of the phenylalanine ammonia-lyase (PAL) gene, which may increase the accumulation of catechins. In conclusion, it is recommended to apply 150 kg/ha of nitrogen fertilizer in the summer and 300 kg/ha of nitrogen fertilizer in the autumn. This recommendation provides a theoretical basis for improving the quality and yield of tea leaves in summer and autumn. [ABSTRACT FROM AUTHOR]

Nitrogen fertilization level and harvesting season significantly impact tea aroma quality. In this study, we analyzed the volatile organic compounds of fresh Jin Xuan (JX) tea leaves under different nitrogen application levels (N0, N150, N300, N450) during summer and autumn. A total of 49 volatile components were identified by gas chromatography–mass spectrometry (GC-MS). Notably, (E)-2-hexenal, linalool, and geraniol were the main contributors to the aroma of fresh JX leaves. The no-nitrogen treatment (N0) presented the greatest quantity and variety of volatiles in both seasons. A greater difference in volatile compounds was observed between nitrogen treatments in summer vs. autumn. The N0 treatment had a greater total volatile concentration in summer, while the opposite was observed in the nitrogen application treatments (N150, N300, N450). Summer treatments appeared best suited to black tea production. The concentration of herbaceous aroma-type volatiles was higher in summer, while the concentration of floral volatiles was higher in autumn. Volatile concentrations were highest in the N0 and N450 treatments in autumn and appeared suitable for making black tea and oolong tea. Overall, this research provides valuable insights into how variations in N application rates across different harvesting seasons impact the aroma characteristics of tea leaves. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effect of different storage temperatures and methods for fresh tea leaves on the sensory quality, aroma components and biochemical components of broken black tea prepared from‘Yinghong 9’ tea leaves stored at 15, 25, 22–28 (room temperature) or 36–37 ℃ using sensory evaluation and headspace solid phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS). The results showed that storage of fresh tea leaves at 15 ℃ could enhance the fresh and brick taste, sweetness and sweet volatile compound contents of broken black tea, which was more favorable to the formation of the quality of broken black tea. However, storage at 36–37 ℃ of fresh tea leaves could significantly affect the sensory quality and quality components of broken black tea. These results provide a reference for fresh tea leaf management and the quality improvement of black broken tea.

Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens.

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aroma plays a pivotal role in defining tea quality and distinctiveness, and tea producers have often observed that specific drought conditions are closely associated with the formation and accumulation of characteristic aroma compounds in tea leaves. However, there is still limited understanding of the differential strategies employed by various tea cultivars in response to drought stress for the accumulation of key volatile aroma compounds in fresh tea leaves, as well as the associated metabolic pathways involved in aroma formation. In this study, two widely cultivated tea cultivars in China, Fuding Dabai (FD) and Wuniuzao (WNZ), were examined to assess the impact of mild field drought stress on the composition and accumulation of key volatile aroma compounds in fresh leaves using headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) and headspace solid phase micro-extraction gas chromatography–mass spectrometry (HS-SPME-GC-MS) technologies. Results revealed that drought stress led to a substantial increase in the diversity of volatile compounds (VOCs) in FD, while WNZ exhibited a notable rise in low-threshold VOC concentrations, amplifying sweet, floral, fruity, and earthy aroma profiles in post-drought fresh leaves. Through partial least squares discriminant analysis (PLS-DA) of HS-GC-IMS and HS-SPME-GC-MS data, integrating variable importance projection (VIP) scores and odor activity values (OAVs) above 1, 9, and 13, key odor-active compounds were identified as potential markers distinguishing the drought responses in the two cultivars. These compounds serve as crucial indicators of the aromatic profile shifts induced by drought, providing insights into the differential metabolic strategies of the cultivars. Additionally, KEGG enrichment analysis revealed 12 metabolic pathways, such as terpenoid biosynthesis, fatty acid synthesis, cutin, suberine, and wax biosynthesis, and phenylalanine metabolism, which may play crucial roles in the formation and accumulation of VOCs in tea leaves under drought stress. These findings provide a comprehensive framework for understanding the cultivar-specific mechanisms of aroma formation and accumulation in tea leaves under mild drought conditions.

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined. [ABSTRACT FROM AUTHOR]

To address the problem of a lack of accurate parameters in the discrete element simulation study of the machine-picked fresh tea leaf mechanized-sorting process, this study used machine-picked fresh tea leaves as the research object, established discrete element models of different fresh tea leaf components in EDEM software version 7.0.0. based on the bonded particle model using three-dimensional scanning inverse-modeling technology, and calibrated the simulation parameters through physical tests and virtual simulation tests. Firstly, the intrinsic parameters of machine-picked tea leaves were measured using physical tests; the physical-stacking tea leaf test was conducted using the cylinder lifting method, the tea leaf repose angle being 32.62° as measured from the stacking images using CAD. With the physical repose angle as the target value, the Plackeet–Burman test, the steepest-ascent test and the Box–Behnken optimization test were conducted in turn, and the results showed that the static friction coefficient between tea leaves, the rolling friction coefficient between tea leaves and the static friction coefficient between tea leaves and PVC have a major effect on the repose angle, and the optimal combination of the three significant parameters was determined. Finally, five simulations were conducted using the optimal combination of parameters, the relative error between the repose angle measured by the simulation test and the physical repose angle being just 0.28%. Moreover, the t-test obtained p > 0.05, indicating that there was no significant difference between the simulation test results and the physical test results. The results showed that the calibrated discrete element simulation parameters obtained could provide a reference for the discrete element simulation study of fresh tea leaves. [ABSTRACT FROM AUTHOR]

Nitrogen fertilization level and harvesting season significantly impact tea aroma quality. In this study, we analyzed the volatile organic compounds of fresh Jin Xuan (JX) tea leaves under different nitrogen application levels (N0, N150, N300, N450) during summer and autumn. A total of 49 volatile components were identified by gas chromatography–mass spectrometry (GC-MS). Notably, (E)-2-hexenal, linalool, and geraniol were the main contributors to the aroma of fresh JX leaves. The no-nitrogen treatment (N0) presented the greatest quantity and variety of volatiles in both seasons. A greater difference in volatile compounds was observed between nitrogen treatments in summer vs. autumn. The N0 treatment had a greater total volatile concentration in summer, while the opposite was observed in the nitrogen application treatments (N150, N300, N450). Summer treatments appeared best suited to black tea production. The concentration of herbaceous aroma-type volatiles was higher in summer, while the concentration of floral volatiles was higher in autumn. Volatile concentrations were highest in the N0 and N450 treatments in autumn and appeared suitable for making black tea and oolong tea. Overall, this research provides valuable insights into how variations in N application rates across different harvesting seasons impact the aroma characteristics of tea leaves.

Copyright of Chinese Journal of Bioprocess Engineering is the property of Chinese Journal of Bioprocess Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, near-infrared spectroscopy (NIRS) combined with a variety of chemometrics methods was used to establish a fast and non-destructive prediction model for the purchase price of fresh tea leaves. Firstly, a paired t-test was conducted on the quality index (QI) of seven quality grade fresh tea samples, all of which showed statistical significance (p < 0.05). Further, there was a good linear relationship between the QI, quality grades, and purchase price of fresh tea samples, with the determination coefficient being greater than 0.99. Then, the original near-infrared spectra of fresh tea samples were obtained and preprocessed, with the combination (standard normal variable (SNV) + second derivative (SD)) as the optimal preprocessing method. Four spectral intervals closely related to fresh tea prices were screened using the synergy interval partial least squares (si-PLS), namely 4377.62 cm−1–4751.74 cm−1, 4755.63 cm−1–5129.75 cm−1, 6262.70 cm−1–6633.93 cm−1, and 7386 cm−1–7756.32 cm−1, respectively. The genetic algorithm (GA) was applied to accurately extract 70 and 33 feature spectral data points from the whole denoised spectral data (DSD) and the four characteristic spectral intervals data (FSD), respectively. Principal component analysis (PCA) was applied, respectively, on the data points selected, and the cumulative contribution rates of the first three PCs were 99.856% and 99.852%. Finally, the back propagation artificial neural (BP-ANN) model with a 3-5-1 structure was calibrated with the first three PCs. When the transfer function was logistic, the best results were obtained (Rp2 = 0.985, RMSEP = 6.732 RMB/kg) by 33 feature spectral data points. The detection effect of the best BP-ANN model by 14 external samples were R2 = 0.987 and RMSEP = 6.670 RMB/kg. The results of this study have achieved real-time, non-destructive, and accurate evaluation and digital display of purchase prices of fresh tea samples by using NIRS technology. [ABSTRACT FROM AUTHOR]

Aim to evaluate the uncertain factors which may influence the accuracy of detection of pesticide residues in fresh tea leaves, a case study of dichlorvos, dimethoate, malathion and fenitrothion residues in fresh tea leaves was carried out using gas chromatography with external standard method. According to the relevant standard and protocol, a mathematical model has been studied. The uncertainties created by the entire experimental process were evaluated systematically. Three A-type evaluation components, including reproducibility of determination, fitness of standard curve and recovery rate, and four B-type evaluation components, including preparation of standard solution, sample weighing and pretreatment, and the measurement of instrument were investigated. The results revealed that the expanded uncertainty of measured values of dichlorvos, dimethoate, malathion and fenitrothion in the spiked tea leaves were 0.021, 0.073, 0.036 and 0.023 mg/kg (k=2, confidence interval P=95%), while the theoretical content were 0.096, 0.39, 0.19 and 0.19 mg/kg, respectively. Preparation of standard solution and fitness of standard curve contributed to the major effect on uncertainty, which should be paid more attention for future determination. Our study provides a reference for the accuracy assessment of method in detection of four organophosphorus pesticide residues in fresh tea leaves.

Objective: The uncertainty of determination of three pyrethroid pesticides （cypermethrin, fenvalerate and deltamethrin） in fresh tea leaves by gas chromatography was evaluated. Methods: A mathematical model was established according to the determination process of the experiment, and the uncertainties caused by various factors in the model were identified. The sources are mainly the following 7 aspects: repeated determination of samples, fitness of standard curve, recovery rate, standard solution preparation, sample weighing and pretreatment, and the measurement of instrument. Results: At the spiking level of 0.10 mg/kg, the expanded uncertainty of measured values of cypermethrin, fenvalerate, and deltamethrin were 0.011, 0.011 and 0.012 mg/kg, respectively. The detection results of three pesticides in fresh tea leaves in 95% confidence interval could be expressed as cypermethrin: （0.093±0.011） mg/kg, fenvalerate: （0.094±0.011） mg/kg, deltamethrin: （0.098±0.012） mg/kg （k=2）. Conclusion: The uncertainties of the method were mainly introduced by standard solution preparation, calibration curve fitting and recovery rate, indicating that these aspects should be controlled strictly in the following sample analysis.

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tea plant (Camellia sinensis) is a widely cultivated cash crop and tea is a favorite functional food in the world. Fresh tea leaves (FTLs) play a critical role in bridging the two fields closely related to tea cultivation and tea processing, those are, tea plant biology and tea biochemistry. To provide a comprehensive overview of the development stages, authorship collaboration, research topics, and hotspots and their temporal evolution trends in the field of FTLs research, we conducted a bibliometric analysis, based on 971 publications on FTLs-related research published during 2001-2021 from Web of Science Core Collection. CiteSpace, R package Bibliometrix, and VOSviewer were employed in this research. The results revealed that the development history can be roughly divided into three stages, namely initial stage, slow development stage and rapid development stage. Journal of Agricultural & Food Chemistry published most articles in this field, while Frontiers in Plant Science held the highest total citations and h-index. The most influential country, institution, and author in this field was identified as China, the Chinese Academy of Agricultural Sciences, and Xiaochun Wan, respectively. FTLs-related research can be categorized into three main topics: the regulation mechanism of key genes, the metabolism and features of essential compounds, and tea plants’ growth and stress responses. The most concerning hotspots are the application of advanced technologies, essential metabolites, leaf color variants, and effective cultivation treatments. There has been a shift from basic biochemical and enzymatic studies to studies of molecular mechanisms that depend on multi-omics technologies. We also discussed the future development in this field. This study provides a comprehensive summary of the research field, making it easier for researchers to be informed about its development history, status, and trends. [ABSTRACT FROM AUTHOR]

A method for simultaneous determination of 79 pesticides residues in fresh tea leaves by ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS/MS) was establish in this study. The fresh tea leaves were ground by high-speed wall breaking and then extracted by acetonitrile solution with NaCl. The liquid supernatant was centrifuged and purified by solid phase extraction of anhydrous magnesium sulfate, C18, PSA and GCB. The scheduled multiple reaction monitoring mode (sMRM) was used for scanning and determination, and quantitative by external standard method. The results show that the 79 target compounds exhibited good linearity within the concentration range of 0.001~0.400 mg⋅L-1 with a correlation coefficient (R²) of 0.990 6 to 0.999 9. The average recoveries at 5 spiked levels (0.01, 0.03, 0.05, 0.10, 0.20 mg⋅kg-1) ranged from 70.7% to 119.0%, with the relative standard deviations (RSDs) ranging from 0.1% to 19.8%, and the limits of quantitation (LOQ) for the 79 pesticides ranging from 0.01 mg⋅kg-1 to 0.05 mg⋅kg-1. The proposed method is high sensitivity, good accuracy and quick, which is suitable for determination of 79 pesticides residues in fresh tea leaves. [ABSTRACT FROM AUTHOR]

Harvest season exerts great influence on tea quality. Herein, the variations in non-volatile flavor substances in spring and summer fresh tea leaves of four varieties were comprehensively investigated by integrating UHPLC-Q-Exactive based lipidomics and metabolomics. A total of 327 lipids and 99 metabolites were detected, among which, 221 and 58 molecules were significantly differential. The molecular species of phospholipids, glycolipids and acylglycerolipids showed most prominent and structure-dependent seasonal changes, relating to polar head, unsaturation and total acyl length. Particularly, spring tea contained higher amount in aroma precursors of highly unsaturated glycolipids and phosphatidic acids. The contents of umami-enhancing amino acids and phenolic acids, e.g., theanine, theogallin and gallotannins, were increased in spring. Besides, catechins, theaflavins, theasinensins and flavone/flavonol glycosides showed diverse changes. These phytochemical differences covered key aroma precursors, tastants and colorants, and may confer superior flavor of black tea processed using spring leaves, which was verified by sensory evaluation. • 327 lipids and 99 metabolites were detected in fresh tea leaves by multi-omics. • 221 lipids and 58 metabolites showed significant seasonal changes in fresh leaves. • Phospholipids and glycerolipids showed great and structure-dependent seasonal change. • Aroma precursors highly-unsaturated glycolipids and PA showed higher content in spring. • Umami-enhancing compounds theanine and gallotannis showed higher content in spring. [ABSTRACT FROM AUTHOR]

To address the problem of a lack of accurate parameters in the discrete element simulation study of the machine-picked fresh tea leaf mechanized-sorting process, this study used machine-picked fresh tea leaves as the research object, established discrete element models of different fresh tea leaf components in EDEM software version 7.0.0. based on the bonded particle model using three-dimensional scanning inverse-modeling technology, and calibrated the simulation parameters through physical tests and virtual simulation tests. Firstly, the intrinsic parameters of machine-picked tea leaves were measured using physical tests; the physical-stacking tea leaf test was conducted using the cylinder lifting method, the tea leaf repose angle being 32.62° as measured from the stacking images using CAD. With the physical repose angle as the target value, the Plackeet–Burman test, the steepest-ascent test and the Box–Behnken optimization test were conducted in turn, and the results showed that the static friction coefficient between tea leaves, the rolling friction coefficient between tea leaves and the static friction coefficient between tea leaves and PVC have a major effect on the repose angle, and the optimal combination of the three significant parameters was determined. Finally, five simulations were conducted using the optimal combination of parameters, the relative error between the repose angle measured by the simulation test and the physical repose angle being just 0.28%. Moreover, the t-test obtained p > 0.05, indicating that there was no significant difference between the simulation test results and the physical test results. The results showed that the calibrated discrete element simulation parameters obtained could provide a reference for the discrete element simulation study of fresh tea leaves.

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

Tea polyphenols are considered as an important indicator of tea quality. Rapid detection of tea polyphenol content plays a valuable role for tea breeding and quality inspection during tea production. In this work, a portable rapid non-destructive detection device of tea polyphenols in fresh tea leaves was developed, which integrated the fusion technology of visible/short-wave (400–1050 nm) and long-wave (1000–1650 nm) near-infrared spectroscopy (Vis/NIR). Experimental results indicated that the spectra within the overlapping region (1000–1050 nm) were assembled by applying the spectral data fusing method. Followed by spectral data preprocessing with the Savitzky–Golay smoothing (SG) method, least squares support vector regression (LS–SVR) models were established for detecting the tea polyphenol content of fresh tea leaves. Based on the fused Vis/NIR spectra (dual-band), the correlation coefficient of calibration (RC), root mean square error of calibration (RMSEC), correlation coefficient of prediction (RP), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) reached 0.976, 0.679%, 0.893, 0.897%, and 2.230, respectively, which were better than the visible/short-wave or long-wave near infrared spectral data (single-band). The sensitive spectral wavebands of tea polyphenols extracted using the random frog (RF) algorithm were distributed in 402–448 nm, 555–600 nm, 810–1042 nm, 1056–1103 nm, 1219–1323 nm, 1406–1416 nm, and 1499–1511 nm. This demonstrated that the prediction of tea polyphenol content using fused spectral data combined with the LS–SVR model depended on various functional groups such as auxochromes, chromogenic groups, and hydrogen-containing groups. The proposed device is capable of non-destructive detection of tea polyphenol content in fresh tea leaves, which can provide effective technical support for tea breeding and tea leaf quality control. [ABSTRACT FROM AUTHOR]

Conventional characterization of catechin content in fresh tea leaves refers to the quantification of (-)-EGCG, (-)- ECG, (-)- EGC, (-)- EC, and (+)- C for their medicinal benefits. For the two tea cultivars S3A3 and TV18, being cultivated in NE India, the article targets a detailed catechin profile and a comparative assessment with the best findings in the literature. The investigations affirmed that the chosen tea cultivar leaves possessed higher constitution of galloyl catechins (EGCG & ECG). Also, while EC and C are in low concentrations, EGC concentration has been very high. Henceforth, both S3A3 and TV18 can be customized as promising sources for the development of functional tea beverage products. [ABSTRACT FROM AUTHOR]

Background: High‐quality tea requires leaves of similar size and tenderness. The grade of the fresh leaves determines the quality of the tea. The automated classification of fresh tea leaves improves resource utilization and reduces manual picking costs. The present study proposes a method based on an improved genetic algorithm for identifying fresh tea leaves in high‐speed parabolic motion using the phenotypic characteristics of the leaves. During parabolic flight, light is transmitted through the tea leaves, and six types of fresh tea leaves can be quickly identified by a camera. Results: The influence of combinations of morphology, color, and custom corner‐point morphological features on the classification results were investigated, and the necessary dimensionality of the model was tested. After feature selection and combination, the classification performance of the Naive Bayes, k‐nearest neighbor, and support vector machine algorithms were compared. The recognition time of Naive Bayes was the shortest, whereas the accuracy of support vector machine had the best classification accuracy at approximately 97%. The support vector machine algorithm with only three feature dimensions (equivalent diameter, circularity, and skeleton endpoints) can meet production requirements with an accuracy rate reaching 92.5%. The proposed algorithm was tested by using the Swedish leaf and Flavia data sets, on which it achieved accuracies of 99.57% and 99.44%, respectively, demonstrating the flexibility and efficiency of the recognition scheme detailed in the present study. Conclusion: This research provides an efficient tea leaves recognition system that can be applied to production lines to reduce manual picking costs. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

In this study, near-infrared spectroscopy (NIRS) combined with a variety of chemometrics methods was used to establish a fast and non-destructive prediction model for the purchase price of fresh tea leaves. Firstly, a paired t-test was conducted on the quality index (QI) of seven quality grade fresh tea samples, all of which showed statistical significance (p < 0.05). Further, there was a good linear relationship between the QI, quality grades, and purchase price of fresh tea samples, with the determination coefficient being greater than 0.99. Then, the original near-infrared spectra of fresh tea samples were obtained and preprocessed, with the combination (standard normal variable (SNV) + second derivative (SD)) as the optimal preprocessing method. Four spectral intervals closely related to fresh tea prices were screened using the synergy interval partial least squares (si-PLS), namely 4377.62 cm−1–4751.74 cm−1, 4755.63 cm−1–5129.75 cm−1, 6262.70 cm−1–6633.93 cm−1, and 7386 cm−1–7756.32 cm−1, respectively. The genetic algorithm (GA) was applied to accurately extract 70 and 33 feature spectral data points from the whole denoised spectral data (DSD) and the four characteristic spectral intervals data (FSD), respectively. Principal component analysis (PCA) was applied, respectively, on the data points selected, and the cumulative contribution rates of the first three PCs were 99.856% and 99.852%. Finally, the back propagation artificial neural (BP-ANN) model with a 3-5-1 structure was calibrated with the first three PCs. When the transfer function was logistic, the best results were obtained (Rp2 = 0.985, RMSEP = 6.732 RMB/kg) by 33 feature spectral data points. The detection effect of the best BP-ANN model by 14 external samples were R2 = 0.987 and RMSEP = 6.670 RMB/kg. The results of this study have achieved real-time, non-destructive, and accurate evaluation and digital display of purchase prices of fresh tea samples by using NIRS technology.

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Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Tea polyphenols are considered as an important indicator of tea quality. Rapid detection of tea polyphenol content plays a valuable role for tea breeding and quality inspection during tea production. In this work, a portable rapid non-destructive detection device of tea polyphenols in fresh tea leaves was developed, which integrated the fusion technology of visible/short-wave (400–1050 nm) and long-wave (1000–1650 nm) near-infrared spectroscopy (Vis/NIR). Experimental results indicated that the spectra within the overlapping region (1000–1050 nm) were assembled by applying the spectral data fusing method. Followed by spectral data preprocessing with the Savitzky–Golay smoothing (SG) method, least squares support vector regression (LS–SVR) models were established for detecting the tea polyphenol content of fresh tea leaves. Based on the fused Vis/NIR spectra (dual-band), the correlation coefficient of calibration (RC), root mean square error of calibration (RMSEC), correlation coefficient of prediction (RP), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) reached 0.976, 0.679%, 0.893, 0.897%, and 2.230, respectively, which were better than the visible/short-wave or long-wave near infrared spectral data (single-band). The sensitive spectral wavebands of tea polyphenols extracted using the random frog (RF) algorithm were distributed in 402–448 nm, 555–600 nm, 810–1042 nm, 1056–1103 nm, 1219–1323 nm, 1406–1416 nm, and 1499–1511 nm. This demonstrated that the prediction of tea polyphenol content using fused spectral data combined with the LS–SVR model depended on various functional groups such as auxochromes, chromogenic groups, and hydrogen-containing groups. The proposed device is capable of non-destructive detection of tea polyphenol content in fresh tea leaves, which can provide effective technical support for tea breeding and tea leaf quality control.

Lactic acid bacteria (LAB) are safe microorganisms that have been used in the processing of fermented food for centuries. The aim of this study was to isolate Lactobacillus from fresh tea leaves and examine the impact of an isolated strain on intestinal barrier integrity. First, the presence of Lactobacillus strains was investigated in fresh tea leaves from Kagoshima, Japan. Strains were isolated by growing on De Man, Rogosa and Sharpe (MRS) agar medium containing sodium carbonate, followed by the identification of one strain by polymerase chain reaction (PCR) and pheS sequence analysis, with the strain identified as Lactiplantibacillus plantarum and named L. plantarum LOC1. Second, the impact of strain LOC1 in its heat-inactivated form on intestinal barrier integrity was investigated. Strain LOC1, but not L. plantarum ATCC 14917T or L. plantarum ATCC 8014, significantly suppressed dextran sulfate sodium (DSS)-induced decreases in transepithelial electrical resistance values of Caco- 2:HT29-MTX 100:0 and 90:10 co-cultures. Moreover, in Caco-2:HT29-MTX co-cultures (90:10 and 75:25), levels of occludin mRNA were significantly increased by strain LOC1 compared with untreated co-cultures, and strain LOC1 had higher mRNA levels of MUC2 and MUC4 mucins than L. plantarum ATCC 14917T and L. plantarum YT9. These results indicate that L. plantarum LOC1 may be used as a safe probiotic with beneficial effects on the intestinal barrier, suggesting that fresh tea leaves could be utilized as a safe source for isolating probiotics. [ABSTRACT FROM AUTHOR]

This paper applies near-infrared spectroscopy (NIRS) and multiple chemometrics to efficiently distinguish the origins of fresh tea leaves. The key components were obtained using the partial least squares discriminant analysis (PLS-DA) method. PLS, synergy interval PLS (siPLS), principal component analysis (PCA), genetic algorithm (GA), and their combination methods were used to establish NIRS non-destructive discrimination models. Then, the practical application was examined using external samples. The study identified nine key components (variable importance for the projection (VIP) > 1): epigallocatechin, epicatechin, total sugar, water extracts, total catechins, gallocatechin gallate, tea polyphenols, gallocatechin, and epigallocatechin gallate. Of the six NIRS models, the siPLS-GA model that used 37 spectral data points produced the best results (Rp2 = 0.9706, RMSEP = 0.0772, RPD = 6.59). This model had a prediction accuracy of 96.67% for the prediction set samples and 93.33% for the external samples. It offers a rapid, precise, and non-invasive approach to monitor and regulate the illicit trade of fresh tea leaves, thereby guaranteeing the authenticity of Enshi Yulu products from the processing source and fostering the long-term prosperity and stability of the Enshi Yulu tea industry. • There is a limited amount of research on using NIRS to trace the origin of fresh tea leaves. • PLS-DA was used to identify the origins of fresh tea leaves based on nine key components. • The NIRS-siPLS-GA digital combination model exhibits high robustness and wide adaptability. • It is a low-cost modeling solution effectively addresses the authenticity problem of Enshi Yulu. • The research findings suggest broad application prospects. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Tea Science is the property of Journal of Tea Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Aminoguanidine (AG), a specific inhibitor of diamine oxidase (DAO), can be used to investigate the γ‐aminobutyric acid (GABA) accumulation pathways. The effect of AG on GABA accumulation pathways of tea fresh leaves remains to be explored. Our results indicated AG concentration did not significantly affect GAD activity, but high concentration solution made GABA‐T activity increase. AG (1 mm) maximally, but not entirely, blocked the polyamine degradation pathway, but had no effect on GAD and GABA‐T. The polyamine degradation pathway was responsible for 26.8% and 17.0% of GABA formation under hypoxic conditions involving immersion and immersion–vacuum, respectively, because DAO and PAO activity was not completely inhibited. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Southern Agriculture is the property of Journal of Southern Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drying is an essential pre-treatment prior to extraction of tea polyphenols from tea leaves, which is a time and energy-intensive process. In this study, pulsed electric field (PEF) was utilized to replace the conventional thermal dehydration procedure before the phenolic extraction. The influence of different PEF conditions on total polyphenol yield from fresh tea leaves combined with a solid-liquid extraction were compared. PEF treatment at 1.00 kV/cm electric field strength, 100 pulses of 100 μs pulse duration, and 5 s pulse repetition, which delivered 22 kJ/kg and induced 1.5 °C of temperature increase, was used for further study on the extraction kinetics of green tea catechins. The results indicated that compared to oven drying, PEF pre-treatment increased the extraction rate by approximately two times, without significantly altering the phenolic profiles, as revealed by using liquid chromatography combined with mass spectrometry. Scanning electron microscopy imaging revealed that PEF pre-treatment induced the formation of inhomogeneously distributed pores and protuberances on the surface of leaf tissues, which might facilitate the penetration of extraction solvent and the migration of phenolics. This study demonstrates that PEF as a time and energy efficient processing method is a promising alternative for the conventional drying process before further tea polyphenol extraction. [ABSTRACT FROM AUTHOR]

The harvesting time of fresh tea leaves has a significant impact on product yield and quality. The aim of this study was to propose a method for real-time monitoring based on machine vision to determine the optimum harvesting time for picking fresh tea leaves. Firstly, the shapes of fresh tea leaves were distinguished from RGB images of the tea-tree canopy after graying with the improved B-G algorithm, filtering with a median filter algorithm, binary processing with the Otsu algorithm, and noise reduction and edge smoothing using open and close operations. Then the leaf characteristics, such as leaf area index, average length, and leaf identification index, were calculated. Based on these, the Bayesian discriminant principle and method were used to construct a discriminant model for fresh tea-leaf collection status. When this method was applied to a RGB tea-tree canopy image acquired at 45° shooting angle, the fresh tea-leaf recognition rate was 90.3%, and the accuracy for fresh tea-leaf harvesting status was 98% by cross validation. Hence, this method provides the basic conditions for future tea-plantation operation and management using information technology, automation, and intelligent systems. [ABSTRACT FROM AUTHOR]

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

The roles of the γ-aminobutyric acid (GABA) shunt and polyamine (PA) degradation pathway on GABA accumulation were investigated in fresh tea leaves under anaerobic and aerobic sequential incubations. The GABA accumulation was mainly completed in the first anaerobic incubation through the interaction of the GABA shunt with the PA degradation pathway. When treated with aminoguanidine, the diamine oxidase and polyamine oxidase activities were almost completely inhibited, and the GABA contents decreased by 40.7%, 46.4%, 41.0% and 37.5% in the tea leaves of the four cultivars ‘Fuyun No. 6’, ‘Fudingdabai’, ‘Longjing No. 43’ and ‘Pingyangtezao’, respectively. The glutamate decarboxylase activity decreased significantly, and the accumulation of GABA may increase or decrease because of varietal differences, which occurred mainly relying on the PA degradation pathway during the second anaerobic incubation. Thus, approximately 37%–47% of the GABA formed in fresh tea leaves under hypoxia was supplied by the PA pathway. [ABSTRACT FROM AUTHOR]

A multiresidue analytical method for the determination of 28 pesticide residues in fresh tea leaves was developed and evaluated by using QuEChERS (quick, easy, cheap, effective, rugged and safe) and triple-quadrupole tandem mass spectrometry (GC-MS/MS). Samples were extracted by acetonitrile, and purified by 75 mg PSA, 25 mg C18 and 4 mg GCB, followed by GC-MS/MS detection under multi-reaction monitoring (MRM) mode and quantified by external standard method. Good linearity was achieved between area size and concentration from 0.020 to 0.40 mg/L, with a coefficient of determination higher than 0.991. The average recovery at different spiking levels (0.10, 0.50 μg/kg) was ranged from 77% to 106% with RSD (relative standard deviation, n = 6) 1.4%-8.0%. The LOD (limit of detection) was 5.0-20 μg/kg, and LOQ (the limit of quantification) was 10-60 μg/kg. The method was simple, quick, accurate, highly sensitive and suitable for the simultaneous analysis of multiresidues of pesticides in fresh tea leaves. [ABSTRACT FROM AUTHOR]

One-dimensional heat and mass transfer mathematical model was developed to simulate moisture content of tea leaves during trough withering. Model solutions were approached using finite difference method with appropriate boundary conditions. A computer program written in QBASIC was used to calculate the real time moisture content of tea leaves and other psychometric parameters of conditioned air during withering. Four experiments were performed using commercial type of withering trough to validate the developed model. Leaf samples were drawn from top, middle and bottom layers of the leaf bed in the trough for one-hour time interval for 12 h of the test period. Simulated moisture data calculated by the program was compared with the experimental data. Time and space increments of the model were chosen minimizing the estimation error of moisture content. Results showed that the experimental and simulated moisture data were in close agreement for top, middle and bottom layers of the withering trough with standard errors in the range of 0.2940–1.2872, 0.7148–1.1025 and 0.7106–4.5478, respectively on percent wet basis. Keywords: Fresh tea leaves; one dimensional heat and mass transfer model; withering trough DOI: http://dx.doi.org/10.4038/tar.v22i3.3701 Tropical Agricultural Research 22(3) (2011) 282-295

The major interest in pulsed electric field treatment of biological tissues is derived from its non-thermal application: increasing cell permeability. This application has an important implication in extraction of complex organic molecules. In this work, pulsed electric field treatment is investigated as a mild (non-thermal) processing method for opening the cell structure in fresh tea leaves. Pulsed electric field utilizes short-duration high-voltage pulses for opening the cell structure by the process called electroporation. Upon the treatment, subsequent extraction of complex organic molecules, particularly, polyphenols, occurs. The amount of extracted polyphenols (in this case, the extraction yield) has been determined as a function of electric field strength, duration and number of applied pulses, as well as energy input per unit of mass of the sample. The results indicate that the used conditions during the treatment increase in temperature did not exceed 10 °C. This limited temperature rise provides a valid evidence that pulsed electric field processing is a non-thermal method applied under used conditions. [ABSTRACT FROM AUTHOR]

A GC-ECD method based on ultrasound assisted extraction (UAE) and dispersive solid phase extraction (DSPE) was established for simultaneous determination of 7 pyrethroid insecticides (fenpropathrin, lambda-cyhalothrin, permethrin, cyfluthrin, cypermethrin, fenvalerate, deltamethrin) in fresh tea leaves. Samples were extracted ultrasonically with V(acetic acid):V(acetonitrile) =1:99, followed by the DSPE clean-up using primary secondary amine (PSA), octadecylsilane bonded silica (C18) and graphitized carbon black (GCB) as adsorbents. The sample extracts were determined by GC-ECD with matrix-matched external standard method. It was shown that good linearity was obtained at concentrations of 0.004-2 mg/L with correlation coefficients higher than 0.999 for these pyrethroids. The average recoveries of the 7 pesticides in fresh tea leaves were in the range of 80% to 101% at spiked levels of 0.01-0.5 mg/kg, with intra-day relative standard deviations lower than 8.4% (n=6) and inter-day relative standard deviations lower than 9.3% (n=3). The limits of detection and quantification were 0.002-0.02 mg/kg and 0.01-0.05 mg/kg, respectively. The method was simple, accurate, solvent saving. It was shown to be a suitable MRM method for determination of pyrethroid residues in fresh tea leaves. [ABSTRACT FROM AUTHOR]

Desorption isotherm of fresh tea leaves was studied using static gravimetric method of saturated salt solutions at temperatures of 15, 20, 25 and 30ºC and water activities of 0.07 to 0.97. Data were fitted to five mathematical equations. Using the best-fit mathematical model, thermodynamic properties of fresh tea leaves including net isosteric heat of sorption, differential entropy were estimated. The applicability of enthalpy - entropy compensation theory for fresh tea leaves was also evaluated. Results showed that the modified Oswin model was best fitted with the experimental data. Thermodynamics properties like net isosteric heat of sorption and differential entropy were found to decrease exponentially with the increase in moisture content. Net isosteric heat of sorption and differential entropy increased exponentially with decreasing moisture content of fresh tea leaves. Isosteric heat of sorption of fresh tea leaves was very close to heat of vaporization of pure water when the moisture content of tea leaves exceeded 40% (d.b.). The enthalpy-entropy compensation theory could be successfully applied for the moisture desorption behavior of fresh tea leaf samples and it was enthalpy driven spontaneous process. [ABSTRACT FROM AUTHOR]

The possibility of using GC-ECD to determine glucosidically bound aroma precursors was investigated. Five β- d-glucopyranosides were derivatized with N-methyl-bis(trifluoroacetamide) (MBTFA) and then determined by GC-ECD, which showed these mono-glycosides (glucosides) could be separated and identified by GC. Repeatability, sensitivity and stability were tested, relative standard deviation (RSD) was in the range of 0.55-5.9%, the limit of detection (LOD) was lower than 10 μg mL, and the standard mixture was stable within 3 days (stored at 4 °C, peak areas RSD < 3%). Application of the developed derivatization and determination method to the fresh tea leaves showed that the glucosides could be accurately quantified (using phenyl β- d-glucopyranoside as internal standard). Because of its high sensitivity, the developed method was suitable for the determination of glucosides in fresh tea leaves. [ABSTRACT FROM AUTHOR]