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Ripening refers to the process of chemical change during the refinement of Keemun black tea (KBT) and is crucial in the formation of Keemun Congou black tea's quality. In this study, the aroma composition of KBT during the ripening was analyzed. Sensomics indicated that ripening strengthened the coconut and fatty aroma of KBT and contributed to the decrease of green aroma substances, resulting in a shift of the overall aroma type of KBT to an integrated aroma profile, which was consistent with sensory evaluation. Changes in fatty acid content and the results of in vitro addition simulation tests confirmed that heat causes highly degradation of fatty acids into fatty aroma volatiles, which is a key driver of the formation of "Keemun aroma" quality. This study revealed the mechanism behind the formation of KBT's integrated "Keemun aroma" quality and the mode of thermal degradation of major fatty acids., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study investigated the changes in the aroma of jasmine tea during storage. Solid-phase micro-extraction (SPME)-gas chromatography (GC)-mass spectrometry (MS) and stir bar sorptive extraction (SBSE)-GC-MS were combined to detect all volatile compounds. GC-olfactometry (GC-O), odor activity value (OAV), and p -value were employed to analyze and identify the key aroma compounds in six jasmine tea samples stored for different durations. Nine key aroma compounds were discovered, namely ( Z )-3-hexen-1-yl acetate, methyl anthranilate, methyl salicylate, trans- β -ionone, linalool, geraniol, ( Z )-4-heptenal, benzoic acid methyl ester, and benzoic acid ethyl ester. The importance of these compounds was confirmed through the aroma addition experiment. Correlation analysis showed that ( Z )-4-heptenal might be the main reason for the increase in the stale aroma of jasmine tea. Through sensory evaluation and specific experimental analysis, it can be concluded that jasmine tea had the best aroma after 3 years of storage, and too long a storage time may cause the overall aroma of the tea to weaken and produce an undesirable odor. The findings can provide a reference for the change in aroma during the storage of jasmine tea and provide the best storage time (3 years) in terms of jasmine tea aroma.

Roasting is a key process in the production of large-leaf yellow tea (LYT). In this study, we synthesized metabolomics and electronic-tongue analysis to compare the quality of charcoal-roasted, electric-roasted and drum-roasted LYT. Charcoal-roasted LYT had the highest yellowness and redness, drum-roasted LYT had a more prominent umami and brightness, and electric roasting reduced astringency. A total of 48 metabolites were identified by metabolomics. Among these, leucocyanidin, kaempferol, luteolin-7-lactate, and apigenin-7- O -neohesperidoside might affect the brightness and yellowness. Theanine, aspartic acid, and glutamic acid contents significantly and positively correlated with umami levels, and the high retention of flavonoid glycosides and catechins in drum-roasted LYT contributed to its astringency. These findings elucidate the contribution of the roasting method to the quality of LYT and provide a theoretical basis for LYT production., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

As a kind of green tea with unique multiple baking processes, the flavor code of Lu'an Guapian (LAGP) has recently been revealed. To improve and stabilize the quality of LAGP, further insight into the dynamic changes in odorants during the whole processing is required. In this study, 50 odorants were identified in processing tea leaves, 14 of which were selected for absolute quantification to profile the effect of processes. The results showed that spreading is crucial for key aroma generation and accumulation, while these odorants undergo significant changes at the deep baking stage. By adjusting the conditions of the spreading and deep baking, it was found that low-temperature (4 °C) spreading for 6 h and low-temperature with long-time baking (final leaf temperature: 102 °C, 45 min) could improve the overall aroma quality. These results provide a new direction for enhancing the quality of LAGP green tea., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The intensity of green tea's floral and sweet flavors was enhanced after being scented by osmanthus (OSGT). However, the mechanism of flavor enhancement by key volatiles remains unknown. Here, the role of key volatiles in OSGT on aroma and taste was explored by sensory experiment-guided flavor analysis. Binary mixed models of (E)-β-ionone, dihydro-β-ionone, and α-ionone showed additive interactions on floral aroma enhancement, the interactions were increased with increasing concentrations. At the concentration in OSGT, binary mixed models of (E)-β-ionone, geraniol, linalool, and γ-decalactone showed additive interactions on sweet aroma enhancement. (E)-β-ionone, geraniol, linalool, and γ-decalactone all significantly increased the perceived intensity of sweetness of sucrose solutions. Additionally, molecular docking revealed the perception mechanism of olfactory and taste receptors to the above characterized volatiles, with hydrogen bonding and hydrophobic interactions being the main interactions. This study highlights the importance of characteristic volatiles in enhancing the flavor of OSGT., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Large-leaf yellow tea (LYT) is processed from both leaves and stems, resulting in a distinctive rice crust-like aroma. Tea stems may contribute differently to the aroma of LYT than leaves. This study aimed to clarify the specific contribution of stems to LYT. The volatile compounds in different components of LYT were extracted and analyzed using a combination of headspace solid-phase microextraction and stir bar sorptive extraction coupled with gas chromatography-olfactory-mass spectrometry. The results revealed high concentrations of compounds with roasty attributes in stems such as 2-ethyl-3,5-dimethylpyrazine (OAV 153-208) and 2-ethyl-3,6-dimethylpyrazine (OAV 111-140). Aroma recombination and addition experiments confirmed that the roasty aroma provided by stems plays a pivotal role in the formation of the distinctive flavor of LYT. This study offers novel insights into the contribution of stems to the aroma of LYT, which can be used for processing and quality enhancement of roasted tea., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Black teas harvested during the summer season usually have the defect of low aroma intensity, resulting in unacceptability from consumers. The shaking and standing (SS) process is key to the production of oolong tea and is believed to significantly improve the aroma quality. However, the specific effects of the shaking process on the aroma quality of black tea have not been elucidated. SSBT has a higher aroma intensity than BT, especially floral and sweet odors. By Aroma Extract Dilution Analysis (AEDA), 27 volatiles with flavor dilution factor (FD) above 8 were selected, of which 20 had odor activity values (OAV) values above 1; among them, 9 floral and sweet volatiles with high OAV were linalool (485 for BT, 918 for SSBT), (E)-β-ionone (389, 699), geraniol (315, 493), β-myrcene (25, 62), (E)-2-hexenal (2, 7), phenylacetaldehyde (44, 75), (Z)-3-hexenyl hexanoate (19, 41), 1-hexanol (9, 26), and 2-phenylethanol (2,3). Aroma reconstitution of these 20 volatiles showed reliable results of floral, sweet, fruity, and roasted odors, further validating the aroma and intensity profiles of the key odorants. Overall, our results reveal that the metabolite mechanism of the SS process improves the aroma quality of black tea, providing a theoretical basis and guiding measures for the production of high-aroma black tea., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The harvest date is a crucial factor in determining tea quality. For Lu'an Guapian (LAGP) tea, Grain Rain period (GRP) represents a pivotal phase in the transformation of tea quality. The sensory evaluation, computer vision and E -tongue revealed that the liquor color score, B and G values of tea infusion were increased during GRP, while the astringency, bitterness intensities and the R value of the tea infusion were decreased. Consequently, the tea infusion exhibited a greener hue and the taste became appropriate during GRP. Non-targeted metabolomics revealed that the majority of amino acids and derivatives was reduced during GRP. Furthermore, flavonoids, in particular flavonol glycosides, exhibited considerable variation during GRP. Finally, nine metabolites were identified as markers for quality transformation during GRP by PLS and Random Forest. This study investigated the quality of LAGP teas during GRP and filled the gap in the variation of LAGP tea quality during GRP., Competing Interests: All authors declare that they have no conflict of interest., (© 2024 The Authors. Published by Elsevier Ltd.)

Flower scenting is an effective way to enhance the aroma of green tea (GT), including those osmanthus scented green tea (OSGT). However, the mechanism of aroma enhancement by scenting is still unclear. Here, the volatiles of GT, OSGT, and osmanthus were detected by GC-MS. The total volatile content of OSGT was significantly increased compared to GT, with the flowery and coconut aromas enhanced. Furthermore, 17 of 139 volatiles were responsible for the enhancement by GC-olfactometry and their absolute odor activity values (OAVs). Aroma recombination, omission and addition experiments showed that dihydro- β -ionone, (E) - β -ionone, (E, E) -2,4-heptadienal, geraniol, linalool, α -ionone, and γ -decalactone were the key aroma volatiles with flowery or coconut aromas. Additionally, the dynamics of the key volatiles (OAVs >1) from different scenting durations were analyzed, proving that the optimal duration was 6-12 h. This study provides new insight into the mechanism of aroma formation during OSGT production., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Piling fermentation (PF) is crucial for Pu-erh tea aroma, yet its microbial and moist-heat impact on aroma quality is poorly understood. Solid-phase microextraction, solvent-assisted flavor evaporation, and gas chromatography-mass spectrometry were used to detected and analyses the samples of sun-green green tea, sterile PF and spontaneous PF. Microbiological action promotes the formation of stale aromas. Moist-heat action promotes the formation of plum-fragrance and sweet aroma. 20 microbial markers and 28 moist-heat markers were screened from 184 volatile components. Combining odor activity values and gas chromatography-olfactometry, 22 aroma-active compounds were screened (1,2,3-trimethoxybenzene, linalool, 1,2,4-trimethoxybenzene …), and analyzed during PF processing. Aroma omission and addition experiments verified its importance. Gallic acid addition experiments successfully verified that microorganisms are the main contributors to the synthesis of methoxybenzenes. Finally, Blastobotrys, Rasamsonia , and Thermomyces showed positive correlation with the synthesis of 1-ethyl-4-methoxybenzene, 1,2,4-trimethoxybenzene, 1,2,3-trimethoxybenzene, and 1,2-dimethoxybenzene. The formation mechanism of Pu-erh tea's aroma was clarified. Exploring microbial and moist-heat effects on Pu-erh tea volatiles and understanding the methoxybenzene formation mechanism using molecular sensory science., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

The quality of green tea deteriorates the longer it is stored. However, there is a lack of accurate and rapid methods for determining the storage period of tea. In this study, hyperspectral imaging (HSI) was used to determine the storage period of green teas stored at 4 °C (set 1) and 25 °C (set 2), and to quantify and visualize the main chemical components (e.g. catechins). In this study, three prediction algorithms were compared, in which partial least squares discriminant analysis outperformed the other models in qualitative discrimination, with 98% and 96% correct discrimination for two sets, respectively. Moreover, quantitative models for ester catechins, simple catechins, and total catechins were developed with Rp > 0.90 and RPD > 1.0, indicating that the models were reliable. Further, a more intuitive visualization of catechin content was achieved. In conclusion, the HSI provides a rapid, non-destructive method to determine the freshness of stored green tea., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Lu'an Guapian (LAGP) tea is one of the most famous teas in China. However, research on its suitable processing varieties is still lacking. This study analyzed the quality of LAGP tea made from three different tea varieties, namely, ' Anhui1 ' (AH1), ' Quntizhong ' (QTZ), and ' Shuchazao ' (SCZ), using molecular sensory science and metabolomics techniques. The results showed that AH1 had a strong floral aroma and the strongest umami flavor, while QTZ had a distinct roasted aroma and a mellow taste. SCZ had a cooked corn-like aroma and the highest bitterness and astringency owing to the high tea polyphenol contents and low free amino acid contents. The study also identified 12 key aroma-active compounds, with trans - beta -ionone and 2-ethyl-3,5-dimethyl-pyrazine contributing the most to floral and roasted aromas, respectively. The results of this study provide a theoretical and practical basis for selecting and breeding high-quality varieties of LAGP tea and stabilizing its quality., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Flowery-like aroma are positive contributors to green tea. Here, the optimal processing conditions for green tea with flowery-like aroma were designed using spreading time, fixation time and drying temperature as three factors designed by response surface methodology (RSM), and the response value of aroma sensory evaluation score. The volatiles in batches of tea samples were analyzed by GC-MS. The optimal quality was obtained with a flowery-like aroma by RSM under a spreading time of 8.97 h, fixation time of 162.3 s, and drying temperature of 103.32 °C. GC-O and odor activity values further revealed floral-like volatiles, including decanal, linalool oxide, β -lonone, geraniol, ( Z )-jasmone, linalool, nonanal, and benzeneacetaldehyde. The recombination of these floral volatiles confirmed the consistency with the floral green tea. Furthermore, the extending spreading duration (8-10 h), reducing fixation duration (160-190 s), and increasing drying temperature (100-115 °C) promote their accumulation in green tea. This study provides new perspectives for the precise enhancement of floral odorants for green tea., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Lu'an Guapian (LAGP) tea, a representative Chinese roasted green tea, undergoes significant changes in taste and aroma during over-fired drying. However, limited studies have been conducted on these effects. This study employed metabolomics and sensory histology techniques to analyze non-volatile and volatile compounds the second drying and pulley liquefied gas drying (PLD) samples. The results revealed that after PLD, the samples exhibited lower umami, bitterness, and astringency; whereas floral, sweet, roasted, cooked corn-like, and cooked chestnut-like aromas became stronger. Among them, the content of (-)-epigallocatechin gallate, glutamic acid, and theogallin, which were closely related to taste, decreased by 4.5 %, 12.3 %, and 10.4 %, respectively. Eight key aroma components were identified as the main contributors to the sample aroma changes: (E)-β-ionone, dimethyl sulfide, (E,E)-2,4-heptadienal, geraniol, linalool, benzeneacetaldehyde, 2-ethyl-3,5-dimethylpyrazine, and hexanal. This study provides a theoretical basis for enhancing the quality of LAGP teas., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Ash is a testing index with both health inspection value and quality decision value, and it is an essential detection item in the import and export trade of tea. To realize the rapid and effective quantitative analysis of ash content in tea, this study proposed the use of a homemade miniature near-infrared (NIR) spectroscopy combined with multivariate analysis for the rapid detection of ash content in black tea. First, NIR data of black tea samples from different countries were acquired and optimized by the spectral preprocessing method. Then, the optimized pre-processed spectral data were used as features, and four feature wavelength selection algorithms, such as competitive adaptive reweighted sampling, iteratively retaining informative variables (IRIV), variable combination population analysis (VCPA)-IRIV, and interval variable iterative space shrinkage approach (IVISSA), were utilized to optimize the feature spectra. Finally, the support vector machine regression (SVR) algorithm was employed to construct the quantitative models of ash content in black tea by combining the optimal wavelengths obtained from the four feature selection methods mentioned above. The experimental results showed that the IVISSA-SVR model had the best performance, with correlation coefficient (R p ), root mean square errors of prediction (RMSEP), and relative prediction deviation (RPD) of 0.9546, 0.0192, and 5.59 for the prediction set, respectively. The results demonstrate that a miniature NIR sensing system combined with chemometrics as an effective analytical tool can realize the rapid detection of ash content in black tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

An optimized yellowing process for yellow tea (YT) was recently developed. The study found that the optimized yellowing process caused a significant increase in sweet and floral aromas by 31.3% and 24.0%, respectively. A total of 21 aroma-active compounds were identified using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-olfactometry (GC-O) combined with sensomics analysis. Quantification of the 15 aroma-active compounds and calculation of odor activity values (OAVs) showed that the OAVs of sweet and floral aroma compounds increased significantly by 986.2% and 46.4%, respectively, after the optimized yellowing process. Sensory-directed aroma reconstitution and omission experiments confirmed that dimethyl sulfide, 3-methylbutanal, β-ionone, β-damascenone, geraniol, phenylacetaldehyde, and linalool were the key odorants in YT after the optimized yellowing process. Odorant addition tests further demonstrated that β-damascenone (OAV 590.4) was the main odorant for YT sweet aroma enhancement, while β-ionone (OAV 884.6) was the main odorant for YT floral aroma enhancement., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Different degrees of roasting result in differences in the quality and flavor of large-leaf yellow tea. The current sensory evaluation and chemical detection methods cannot meet the requirement of online differentiation of LYT roasting degree, so an accurate and comprehensive assessment method needs to be developed urgently. First, the two aroma sensing technologies were compared. Two variable screening methods and three recognition algorithms were employed to build discriminant models. The results showed that the discrimination rate of the colorimetric sensor array (CSA) in the prediction set reached 91.89 %, outperforming that of the E-nose. Subsequently, three fusion strategies were applied to improve the discrimination accuracy. The discrimination rate of the middle fusion strategy resulted in an optimal resolution of 94.59 %. The results obtained from the homologous fusion were able to evaluate the roasting degree comprehensively and accurately, which provides a new method and idea for tea aroma quality., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Fermented tea (FT) using a single Eurotium cristatum strain can produce a pleasant fungal-flowery aroma, which is similar to the composite aroma characteristic of minty, flowery, and woody aromas, but its molecular basis is not yet clear. In this study, solvent-assisted flavor evaporation and gas chromatography-mass spectrometry/olfactometry were applied to isolate and identify volatiles from the FT by E. cristatum . The application of an aroma extract dilution analysis screened out 43 aroma-active compounds. Quantification revealed that there were 11 odorants with high odor threshold concentrations. Recombination and omission tests revealed that nonanal, methyl salicylate, decanoic acid, 4-methoxybenzaldehyde, α -terpineol, phenylacetaldehyde, and coumarin were the major odorants in the FT. Addition tests further verified that methyl salicylate, 4-methoxybenzaldehyde, and coumarin were the key odorants for fungal-flowery aroma, each corresponding to minty, woody, and flowery aromas, respectively. 4-Methoxybenzaldehyde and coumarin were newly found odorants for fungal-flowery aroma in FT, and 4-methoxybenzaldehyde had not been reported as a tea volatile compound before. This finding may guide future industrial production optimization of FT with improved flavor.

In this study, an optimized yellowing process for yellow tea (YT) was developed by response surface methodology. The results showed that increasing the yellowing temperature from 20 °C to 34 °C, increasing the relative humidity from 55% to 67%, and reducing the yellowing time from 48 h to 16 h, caused a 40.5% and 43.2% increase in the yellowness and sweetness of YT, respectively, and improved the consumer acceptability by 36.8%. Moreover, metabolomics was used to explore the involved mechanisms that resulted in the improved YT quality. The optimized yellowing promoted the hydrolysis of 5 gallated catechins, 6 flavonoid glycosides, theogallin and digalloylglucose, resulting in the accumulation of 5 soluble sugars and gallic acid. Meanwhile, it promoted the oxidative polymerization of catechins (e.g., theaflagallin, δ-type dehydrodicatechin and theasinensin A), but decelerated the degradation of chlorophylls. Overall, this optimized yellowing process could serve as a guide to a shorter yellowing cycle., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Traceability and authentication of protected designation of origin (PDO) tea is an important prerequisite to safeguard its production and distribution system. Here, indicator displacement array (IDA) sensors consisting of natural anthocyanidins and edible metal ions were developed to authenticate PDO and non-PDO Longjing from different origins. Five IDA elements were selected for constructing sensors, achieved by an indicator displacement reaction after adding epigallocatechin gallate solution. The obtained sensors were subsequently used for real tea samples. Unsupervised algorithms were used for data exploration among PDO and non-PDO teas. The supervised support vector machine (SVM) model further achieved accurate authentication of PDO and non-PDO Longjing with a correct classification rate of 100% for the 26 validated samples. The developed IDA sensor thus achieves accurate authentication of PDO tea in a hazard-free and cost-efficient way, providing a useful tool for origin authentication of other agricultural products., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Over-fired drying, a crucial process in the production of Lu'an Guapian (LAGP) tea, greatly enriches the tea's aroma. In this study, the aroma compounds of LAGP tea processed through pulley charcoal drying (PCD), roller drying (RD), roller-conveyor drying (RCD), and hot air drying (HD) were analyzed using gas chromatography-mass spectrometry. A subsequent analysis of aroma extraction dilution analysis and odor activity values revealed that (E)-β-ionone, dimethyl sulfide, (E,E)-2,4-heptadienal, geraniol, linalool, benzeneacetaldehyde, coumarin, 2-ethyl-3,5-dimethyl-pyrazine, indole, hexanal, (Z)-jasmone, and (Z)-3-hexen-1-ol were the key contributors to the samples' aroma variation. Moreover, a quantitative descriptive analysis and aroma recombination and omission experiments analysis revealed that (E)-β-ionone is the most critical contributor to the formation of floral aroma in tea processed using PCD, whereas (E,E)-2,4-heptadienal is responsible for the more pronounced fresh aroma in tea processed using HD. In addition, 2-ethyl-3,5-dimethyl-pyrazine contributes to the formation of a roasted aroma in tea processed using RD and RCD. The study results provide a theoretical basis for choosing the processing method, especially for drying, to obtain high-quality LAGP tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The narrow geographical traceability of green tea is both important and challenging. This study aimed to establish multi-technology metabolomic and chemometric approaches to finely discriminate the geographic origins of green teas. Taiping Houkui green tea samples were analyzed by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry and 1 H NMR of polar (D 2 O) and non-polar (CDCl 3 ). Common dimension, low-level and mid-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of samples from different origins. In assessments of tea from six origins, the single instrument data test set results in 40.00% to 80.00% accuracy. Data fusion improved single-instrument performance classification with mid-level data fusion to obtain 93.33% accuracy in the test set. These results provide comprehensive metabolomic insights into the origin of TPHK fingerprinting and open up new metabolomic approaches for quality control in the tea industry., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

To develop a comprehensive evaluation method for Keemun black tea, we used micro-near-infrared spectroscopy, computer vision, and colorimetric sensor array to collect data. We used support vector machine, least-squares support vector machine (LS-SVM), extreme learning machine, and partial least squares discriminant analysis algorithms to qualitatively discriminate between different grades of tea. Our results indicated that the LS-SVM model with mid-level data fusion attained an accuracy of 98.57% in the testing set. To quantitatively determine flavour substances in black tea, we used support vector regression. The correlation coefficient for the predicted sets of gallic acid, caffeine, epigallocatechin, catechin, epigallocatechin gallate, epicatechin, gallocatechin gallate and total catechins were 0.84089, 0.94249, 0.94050, 0.83820, 0.81111, 0.82670, 0.93230, and 0.93608, respectively. Furthermore, all compounds exhibited residual predictive deviation values exceeding 2. Hence, combining spectral, shape, colour, and aroma data with mid-level data can provide a rapid and comprehensive assessment of Keemun black tea quality., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Aroma affects the quality of black tea, and the rapid evaluation of aroma quality is the key to realize the intelligent processing of black tea. A simple colorimetric sensor array coupled with a hyperspectral system was proposed for the rapid quantitative detection of key volatile organic compounds (VOCs) in black tea. Feature variables were screened based on competitive adaptive reweighted sampling (CARS). Furthermore, the performance of the models for VOCs quantitative prediction was compared. For the quantitative prediction of linalool, benzeneacetaldehyde, hexanal, methyl salicylate, and geraniol, the CARS-least-squares support vector machine model's correlation coefficients were 0.89, 0.95, 0.88, 0.80, and 0.78, respectively. The interaction mechanism of array dyes with VOCs was based on density flooding theory. The optimized highest occupied molecular orbital levels, lowest unoccupied molecular orbital energy levels, dipole moments, and intermolecular distances were determined to be strongly correlated with interactions between array dyes and VOCs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Aroma types of green teas associate with their commercial prices and consumer acceptance, mainly including floral-like (HX), chestnut-like (LX), and fresh (QX) aromas. However, the volatile differences and specificities in these aroma types are still unclear. Herein, Taiping Houkui green teas with HX, LX, and QX aromas were processed separately with the same fresh tea leaves. Ninety-four and seventy-eight volatiles were detected and identified by headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), respectively. Candidate differential volatiles among the tea samples were determined by the variable importance in projection (VIP) of the partial least squares-discriminant analysis (PLS-DA) and were further confirmed by the relative odor activity value (ROAV) and odor description. The volatiles 1-hexanol, linalool oxide (furanoid), linalool, geraniol, (E)-β-ionone, isoamyl acetate, and 2-methylpropanal enriched in HX and contributed to the floral-like aroma, while 3-methylbutanal, 2-ethyl-1-hexanol, indole, β-damascone, and cedrol enriched in LX and contributed to the chestnut-like aroma. This study reveals the specificities and contributions of volatiles in green teas with different aromas, thus providing new insights into the molecular basis of different flavored teas, benefiting for their precision processing and targeted quality control., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The aim of this study was to reveal the molecular basis of aroma changes during storage of An tea (AT). The key volatile compounds in AT were screened using SPME-GC-MS and SPE-GC-MS analytical techniques in combination with odor activity value (OAV) and flavor dilution factor (FD). The results showed that with the increase of storage time the stale and woody aromas were revealed. Esters, acids and hydrocarbons are the main types of volatile compounds in AT, and their content accounts for 52.69 %-61.29 % of the total volatile compounds. The key volatile compounds with stale and woody aromas during AT storage were obtained by OAV value and FD value, namely ketoisophorone (flavor dilution factor, FD = 64), linalool oxide C (FD = 64), 1-octen-3-ol (OAV > 1, FD = 32), 1,2-dimethoxybenzene (FD = 16), naphthalene (OAV > 1, FD = 32), 3,4-dimethoxytoluene (FD = 16), and 1,2,3-trimethoxybenzene (FD = 8). Our research provides a scientific basis and insights for the improvement of quality during the storage of dark tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Aging of green tea leads to reductions in its flavor and health value, yet in situ testing methods for green tea freshness are lacking. A novel sensitive indicator displacement assay (IDA) sensor was constructed and applied for monitoring of green tea freshness during storage. Low-cost pH dyes and metal ions were used as indicators and receptors, respectively, for the targeted detection of catechins in tea samples. The feasibility of the IDA reaction was verified using images and UV-vis spectroscopy, respectively. IDA combined with supervised algorithms achieved accurate identification of green tea freshness with an accuracy of 86.67%, and acceptable accuracies in the prediction of catechin monomers and total catechins with ratio of prediction to deviation values over 1.5. Thus, the developed IDA sensor is capable of qualitative and quantitative monitoring of the green tea freshness during storage, providing a new option for quality evaluation and control of green teas., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The volatile fraction was isolated from the premium and common grade of Lu'an Guapian green tea infusion by solvent-assisted flavor evaporation distillation. With the application of aroma extract dilution analysis, a total of 52 aroma-active compounds were revealed in the flavor dilution (FD) factor area of 32-8192. Besides, five additional odorants with higher volatility were identified using solid-phase microextraction. The aroma profiles, FD factors, and quantitative data of premium Guapian (PGP) and common Guapian (CGP) showed apparent differences. The intensity of the flowery attribute was significantly higher in PGP than in CGP, while cooked vegetable-like was the most outstanding odor note in CGP. The recombination experiment and the omission test of PGP clarified that dimethyl sulfide, ( E , E )-2,4-heptadienal, ( E )-β-ionone, ( E , Z )-2,6-nonadienal, 2-methylbutanal, indole, 6-methyl-5-hepten-2-one, hexanal, 3-methylbutanal, γ-hexalactone, methyl epijasmonate, linalool, geraniol, and ( Z )-3-hexen-1-ol were the key odorants of PGP tea infusion. The omission and addition tests of flowery odorants manifested that ( E )-β-ionone, geraniol, and ( E , E )-2,4-heptadienal, with higher odor activity values in PGP than in CGP, contributed to the flowery attribute most. The difference in the concentration of the abovementioned odorants with flowery odor notes could be one of the main factors which led to the difference in aroma quality between the two grades of Lu'an Guapian .

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

Herein, a new indicator-displacement array (IDA) sensor was developed for the quality evaluation of black tea fermentation. On the principle of the reversible covalent binding of phenylboronic acid and catechol, phenylboronic acids were selected as acceptors for targeted binding to polyphenols. Pyrocatechol violet and alizarin red S were used as indicators of the reaction. The IDA sensors have sensitive differential responses to fermented tea samples, achieving an assessment of the fermentation degree with accuracies of 80.39-88.00% by support vector machine (SVM). In addition, the key polyphenol components of the fermentation process were accurately predicted by the IDA and SVM regression with ratio of prediction to deviation values of 1.55-1.72, 2.03-2.21, and 2.03-2.08 for total polyphenols, total catechins, and epigallocatechin-3-O-gallate, respectively. In conclusion, the developed IDA sensor is capable of the in-situ quality monitoring of black tea fermentation, with the advantages being cost-effectiveness, sensitivity, and rapidity., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Fermentation is a key black tea processing step and makes an important contribution to quality formation. Current approaches to fermentation monitoring are costly or laboratory-based. Here, we first evaluated the potential of at-line computer vision for detecting fermentation quality in a tea factory. A self-built industrial camera was used to collect tea samples at various fermentation durations. The correlations of color variables that were extracted from the images with key quality indicators in the tea samples were verified. Subsequently, partial least-squares regression models based on the color variables showed high prediction accuracy with residual prediction deviation values of 4.13, 3.53, and 3.39 for catechins, theaflavins and chlorophylls, respectively. Finally, the spatial and temporal distributions of indicators during fermentation were mapped to visualize the fermentation quality. This study realized low-cost, at-line and real-time detection for black tea fermentation, which provides technical support for the industrial and intelligent production of black tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This study synthesized stable and sensitive hemp spherical AgNPs as the SERS substrate for the simultaneous and rapid detection of sunset yellow, lemon yellow, carmine and erythrosine adulteration in black tea. With R6G as the probe molecule, the AgNPs were determined to have satisfactory stability over 60 days with an enhancement factor of 10 8 . The effects of three variable screening methods on model performance were compared. Among them, CARS-PLS exhibited superior performance for the quantification of all the four colorants, with prediction set correlation coefficients of 0.95, 0.97, 0.99 and 0.88, respectively. The differentiation of the mixed colorants was also achieved, with recoveries ranging from 91.87 % to 106.5 % with RSD value <1.97 %, demonstrating the high accuracy and precision of the proposed method. The results indicate that AgNPs-based SERS is an effective method and has substantial potential for application in the identification and quantification of colorant in tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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

The mechanism through which solar withering (SW) affects the quality of white tea is unclear. To address this gap in the literature, in this study, we used metabolomics and transcriptomics to investigate the effect of SW on the quality of WT. WT that underwent SW was slightly more bitter and astringent than WT that underwent natural withering (control group). Specifically, SW considerably increased the concentration of astringent flavonoids and flavone glycosides in WT. This increase was mainly attributed to the upregulated expression of key genes in the shikimic acid, phenylpropanoid, and flavonoid biosynthesis pathways, such as shikimate kinase, chalcone synthase, and flavonol synthase. In addition, SW experienced considerable heat and light stress. The levels of glycerophosphatidylcholine and carbohydrates increased in response to the stress, which also affected the taste of WT. The results of this study indicate the mechanism through which SW affects the quality of WT., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Strip green tea (SGT) is widely distributed in China owing to its unique appearance and aroma but the evolution and formation mechanisms of volatile metabolites (VMs) during SGT processing, and especially in the unique process of rubbing, remain unclear. In this study, based on untargeted metabolomics, 217 VMs (8 categories) were identified, and fixation and rubbing processes were found to be key for SGT aroma formation. Moreover, targeted metabolomics was applied to obtain 38 differential VMs and their related substances, of which fatty acid-derived volatiles (14 VMs) and glycoside-derived volatiles (8 VMs) showed significant contributions to SGT aroma, and their derivation laws during SGT manufacturing were clarified. Furthermore, the effect of rubbing degree on volatile metabolite formation was explored, and 11 key differential VMs were screened by variable importance in projection, and odor activity value analyses. Appropriate rubbing promoted the loss of grassy VMs (such as 1-octanol and 2-pentyl-furan) and enrichment of floral/fruity VMs (such as trans-β-ionone, nonanal, geraniol, citral, (Z)-3,7-dimethyl-2,6-octadien-1-ol, and (Z)-hexanoic acid, 3-hexenyl ester). Our study not only enriches the chemical theory of green tea processing but also provides technical support for the precision directional processing of high-quality SGT., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Withering is a key process that affects the aroma of Keemun black tea (KBT). In this study, the aroma composition of KBT through natural withering, sun withering, and warm-air withering was analysed using gas chromatography-mass spectrometry. The results revealed significant differences in the three samples. Gas chromatography-olfactometry and aroma extract dilution analysis were performed with screening through a relative odour activity value (rOAV) > 1. In total, 11 aroma-active compounds (geraniol, (Z)-4-heptenal, 1-octen-3-ol, (E)-β-ionone, 3-methylbutanal, linalool, β-damascenone, (E, E)-2,4-decadienal, methional, (E, E)-2,4-nonadienal, and (E)-2-nonenal) were found to be responsible for the differences in aroma caused by different withering methods. Linalool (rOAV, 161) and geraniol (rOAV, 785) were responsible for the higher flowery and fruity aromas when sun withering was applied, whereas methional (rOAV, 124) contributed to the intense roasty aroma when warm-air withering was employed. Moreover, our results were verified by quantitative descriptive analysis and addition experiments., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Most studies focus on the geographically larger production areas in tea traceability. However, famous high-quality tea is often produced in a narrow range of origins, which makes traceability a challenge. In this study, Taiping Houkui (TPHK) green tea of narrow geographical origin was rapidly identified using Fourier-transform near-infrared (FT-NIR) spectroscopy., Results: First, spectral information of 114 TPHK samples from four production areas was acquired. Second, the synthetic minority over-sampling technique (SMOTE) was used to balance the sample data set, and three different spectral pre-processing methods were compared. Third, three feature variable selection algorithms were used to obtain the pre-processed spectral features. Finally, extreme learning machine (ELM) models based on the variables obtained from the selected features were established to trace the TPHK origin. The optimized ELM model achieves 95.35% classification accuracy in the test set., Conclusion: The present study demonstrates that the optimized variable selection method in combination with NIR spectroscopy represents a suitable strategy for tea traceability in narrow regions. © 2022 Society of Chemical Industry., (© 2022 Society of Chemical Industry.)

Summer green tea (SGT) has poor flavor due to its high levels of bitterness and astringency. The present study aimed to improve the flavor of SGT using the yellowing process. The results showed that after the yellowing process, the sweetness and overall acceptability increased, and the content of gallated catechins and flavonol glycosides decreased by 30.2% and 27.4%, respectively, as did the bitterness and astringency of SGT. Yellowing caused a decrease in the concentration of some aroma compounds, such as (z)-3-hexen-1-ol, 1-hexanol, pentanal, heptanal and 1-octanol, which caused grassy, floral and fruity aromas. In contrast, the concentrations of 1-octen-3-ol, benzene acetaldehyde and β-ionone increased, which have mushroom and sweet aromas. Meanwhile, the sweetness and umami of SGT were enhanced by the addition of selected aroma compounds (1-octen-3-ol, benzene acetaldehyde and β-ionone), demonstrating that the yellowing process improves the flavor of SGT through odor-taste interactions., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

White tea (WT) is a slightly fermented tea, and withering is a critical step in its processing. The withering treatment can affect white tea's aroma; different treatments' effects were investigated in this study. White tea was withered indoors (IWT), in a withering-tank (WWT), or under sunlight (SWT). Quantitative descriptive analysis (QDA) results showed that SWT had a more obvious flower aroma, and WWT had a more pronounced grassy aroma. Volatile compounds were extracted and subsequently detected with solvent-assisted flavor evaporation (SAFE) and headspace solid-phase microextraction (HS-SPME) combined in addition to gas chromatography-mass spectrometry (GC-MS). A total of 202 volatile compounds were detected; 35 of these aroma-active compounds met flavor dilution (FD) factor ≥ 4 or odor activity value (OAV) ≥ 1. The nine key potent odorants for which both conditions were met were dimethyl sulfide , 2-methyl-butanal , 1-penten-3-one , hexanal , (Z)-4-heptenal , β-Myrcene , linalool, geraniol, and trans-β-ionone. These results were used with QDA to reveal that SWT had a stronger floral aroma mainly due to an increase of geraniol and linalool. Moreover, WWT had a stronger grassy aroma mainly due to increased hexanal . The results could be used to select processing methods for producing white tea with a superior aroma.

Rapid monitoring of fermentation quality has been the key to realizing the intelligent processing of black tea. In our study, mixing ratios, sensing array components and reaction times were optimized before an optimal solution phase colorimetric sensor array was constructed. The characteristic spectral information of the array was obtained by UV-visible spectroscopy and subsequently combined with machine learning algorithms to construct a black tea fermentation quality evaluation model. The competitive adaptive reweighting algorithms (CARS)-support vector machine model discriminated the black tea fermentation degree with 100% accuracy. For quantification of catechins and four theaflavins (TF, TFDG, TF-3-G, and TF-3'-G), the correlation coefficients of the CARS least square support vector machine model prediction set were 0.91, 0.86, 0.76, 0.72 and 0.79, respectively. The results obtained within 2 min enabled accurate monitoring of the fermentation quality of black tea, which provides a new method and idea for intelligent black tea processing., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Withering is one of the most critical steps in the processing of black tea. The degree of withering affects the aroma quality of the finished tea. In this study, we used a pH indicator-based colorimetric sensor array in combination with hyperspectral imaging to intelligently evaluate the withering degree. After analyzing the difference between images taken before and after the reaction of pH indicators with withered leaves, six pH indicators were selected to build a sensor array. Then, the hyperspectral image of each pH indicator was obtained at wavelengths between 400 and 1000 nm. Nonlinear support vector machine (SVM) and least-squares (LS) SVM models were established to determine the degree of withering. Results revealed that the spectral information from single pH indicator failed to accurately evaluate the withering degree. The LS-SVM model achieved satisfactory discriminant results with the low-level data fusion of six pH indicators followed by principal component analysis for dimensionality reduction. The optimal model yielded accuracies of 93.75% and 90.00% for the calibration and prediction sets, respectively. The results indicated that colorimetric sensor array in combination with hyperspectral imaging can effectively determine the withering degree, thus providing a novel method for the intelligent processing of food and tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The geographical origin and processing month of green tea greatly affect its economic value and consumer acceptance. This study investigated the feasibility of combining near-infrared hyperspectral imaging (NIR-HSI) with chemometrics for the identification of green tea. Tea samples produced in three regions of Chongqing (southeastern Chongqing, northeastern Chongqing, and western Chongqing) for four months (from May to August 2020) were collected. Principal component analysis (PCA) was used to reduce data dimensionality and visualize the clustering of samples in different categories. Linear partial least squares-discriminant analysis (PLS-DA) and nonlinear support vector machine (SVM) algorithms were used to develop discriminant models. The PCA-SVM models based on the first four and first five principal components (PCs) achieved the best accuracies of 97.5% and 95% in the prediction set for geographical origin and processing month of green tea, respectively. This study demonstrated the feasibility of HSI in the identification of green tea species, providing a rapid and nondestructive method for the evaluation and control of green tea quality., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Intelligent identification of black tea fermentation quality is becoming a bottleneck to industrial automation. This study presents at-line rapid detection of black tea fermentation quality at industrial scale based on low-cost micro-near-infrared spectroscopy (NIRS) and laboratory-made computer vision system (CVS). High-performance liquid chromatography and a spectrophotometer were used for determining the content of catechins and theaflavins, and the color of tea samples, respectively. Hierarchical cluster analysis combined with sensory evaluation was used to group samples through different fermentation degrees. A principal component analysis-support vector machine (SVM) model was developed to discriminate the black tea fermentation degree using color, spectral, and data fusion information; high accuracy (calibration = 95.89%, prediction = 89.19%) was achieved using mid-level data fusion. In addition, SVM model for theaflavins content prediction was established. The results indicated that the micro-NIRS combined with CVS proved a portable and low-cost tool for evaluating the black tea fermentation quality., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Crush-tear-curl (CTC) black tea is a popular beverage, owing to its unique taste characteristics and health benefits. However, differences in the taste quality and chemical profiles of CTC black tea from different geographical regions remain unclear. In this study, 28 CTC black tea samples were collected from six geographical regions and analyzed using electronic tongue and ultrahigh performance liquid chromatography-Orbitrap-mass spectroscopy. The e-tongue analysis indicated that each region's CTC black tea has its own relatively prominent taste characteristics: Sri Lanka (more umami and astringent), North India (more umami), China (more sweetness and astringent), South India (moderate umami and sweetness), and Kenya (moderate umami and astringent). Based on multivariate statistical analysis, 78 metabolites were tentatively identified and used as potential markers for CTC black tea of different origins, mainly including amino acids, flavone/flavonol glycosides, and pigments. Different metabolites, which contributed to the taste characteristics of CTC black tea, were clarified by partial least squares regression correlation analysis. Our findings may serve as useful references for future studies on origin traceability and quality characteristic determination of CTC black teas. PRACTICAL APPLICATION: This study provides useful references for future studies on the origin traceability and taste characteristic determination of CTC black teas from different geographical regions., (© 2021 Institute of Food Technologists®.)

The aroma profile of raw pu'er tea (RPT) depends on its storage duration (2-10 years) and storage conditions (wet-hot or dry-cold environment). We analyzed the major odorants of RPT samples by performing metabolomic analysis and by using the molecular sensory science approach. Under dry-cold storage conditions, tea leaves had more carotenoid derivatives, glycoside-derived volatiles, and phenolic volatiles, resulting in "fresh," "floral," and "sweet" aroma. Under wet-hot storage conditions, tea leaves had more methoxybenzenes, which contributed considerably to their "stale" and "woody" aroma. We identified 11 and 4 compounds as the odor markers of RPTs when stored in dry-cold and wet-hot environments, respectively. Our findings provide a scientific basis for optimal storage that yields the desired aroma profile., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Rapid and low-cost testing tools provide new methods for the evaluation of tea quality. In this study, a micro near-infrared (NIR) spectrometer was used for the qualitative and quantitative evaluation of tea. A total of 360 tea samples consisting of black, green, yellow, and oolong tea were collected from different countries. Chemometrics including linear partial least squares (PLS) regression, PLS discriminant analysis, and nonlinear radial basis function-support vector machine (RBF-SVM) were used. The RBF-SVM model achieved optimal discriminant performance for tea types with a correct classification rate of 98.33%. Wavelength selection of iteratively variable subset optimization (IVSO) exhibited considerable advantages in improving the predictive performance of catechin, caffeine, and theanine models. The IVSO-PLS regression models achieved satisfactory results for catechins and caffeine prediction, with Rp over 0.9, and RPD over 2.5. Thus, the study provided a portable and low-cost method for in-situ assessing tea quality., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Keemun black tea is classified into 7 grades according to the difference in its quality. The appearance and flavour are crucial indicators of its quality. This research demonstrates a rapid grading method of jointly using near-infrared reflectance spectroscopy (NIRS) and computer vision systems (CVS) to evaluate the flavour and appearance quality of tea. A Bruker MPA Fourier Transform near-infrared spectrometer was used to record the spectrum of samples. A computer vision system was used to capture the image of tea leaves in an unobstructed manner. 80 tea samples for each grade were analyzed. The performance of four NIRS feature extraction methods (principal component analysis, local linear embedding, isometric feature mapping, and convolutional neural network (CNN)) was compared in this study. Histograms of six geometric features (leaf width, leaf length, leaf area, leaf perimeter, aspect ratio, and rectangularity) of different tea samples were used to describe their appearance. A feature-level fusion strategy was used to combine softmax and artificial neural networks (ANN) to classify NIRS and CVS features. The results indicated that for an individual NIRS signal, CNN achieved the highest classification accuracy with the softmax classification model. The histograms of the combined shape features indicated that when the softmax classification model was used, the classification accuracy was also higher than ANN. The fusion of NIRS and CVS features proved to be the optimal combination; the accuracy of calibration, validation and testing sets increased from 99.29%, 96.67% and 98.57% (when the optimal features from a single-sensor were used) to 100.00%, 99.29% and 100.00% (when features from multiple-sensors were used). This study revealed that the combination of NIRS and CVS features can be a useful strategy for classifying black tea samples of different grades., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Tea (Camellia sinensis L) is a highly nutritious beverage with commercial value globally. However, it is at risk of economic fraud. This study aims to develop a powerful evaluation method to distinguish Chinese official Dianhong tea from various other categories, employing hyperspectral imaging (HSI) technology and chemometric algorithms., Results: Two matrix statistical algorithms encompassing a gray-level co-occurrence matrix (GLCM) and a gradient co-occurrence matrix (GLGCM) are used to extract HSI texture data. Three novel spectral variable screening methods are utilized to select wavenumbers of near-infrared (NIR) spectra: iteratively retaining informative variables (IRIV), interval random frog, and variable combination population analysis. Feature fusion of image texture characteristics and spectra data are the eigenvectors for model building. Authentic classification models are constructed using the extreme learning machine approach and the least squares support vector machine (LSSVM) approach, coupling them with features from wavelength extraction techniques for assessing the quality of Dianhong black tea. The results demonstrate that the LSSVM model using fused data (IRIV + GLGCM) provides the best results and achieves a predictive precision of 99.57%., Conclusion: This study confirms that HSI coupled with LSSVM is effective in differentiating authentic Dianhong black tea samples. © 2020 Society of Chemical Industry., (© 2020 Society of Chemical Industry.)

Green tea adulterated with sugar and glutinous rice flour has an increased sensitivity to water, which affects the safety of the tea. A total of 475 samples of pure tea, sugar-adulterated tea, and glutinous-rice-flour-adulterated tea were prepared and scanned using micro near infrared spectroscopy (NIRS). The collected NIRS data were qualitatively and quantitatively detected by a multi-layer algorithm model. Principal component analysis indicated that the three sample groups had an obvious separation trend. The discriminate rate of the optimal qualitative model, namely support vector machine, was 97.47% for the prediction set. A total of three wavelength selection methods were used to improve the performances of partial least squares regression and support vector machine regression (SVR) models. The nonlinear SVR models based on characteristic wavelengths selected by iteratively retaining informative variables algorithm provided satisfactory results for the identification of sugar and glutinous rice flour adulteration. The correlation coefficients for prediction (Rp) were >0.94, and the residual prediction deviation were >3. The results indicated that smartphone-based micro NIRS can be effectively used to qualitatively and quantitatively analyze adulterants in green tea., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Tea quality is generally assessed through panel sensory evaluation, which requires elaborate sample preparation steps. Here, a novel and low-cost evaluation method of using smartphone imaging coupled with micro-near-infrared (NIR) spectrometer based on digital light processing is proposed to classify the quality grades of Keemun black tea. RGB color information was obtained by Image J software, eight texture characteristics, including scheme, contrast, dissimilarity, entropy, correlation, second moment and variance, and homogeneity were obtained by ENVI software based on co - occurrence method from smartphone images, and spectral data were preprocessed with standard normal variate. A principal component analysis (PCA)-support vector machine (SVM) model was established to analyze the color, texture, and spectral data. Low-level and middle-level fusion strategies were introduced for analyzing the fusion data. The results indicated that the accuracy of the SVM model on mid-level data fusion (100.00%, 94.29% for calibration set and prediction set, respectively) was higher than that obtained for separate color (97.14%, 88.57%), texture (84.29%, 60%), spectrum (74.29%, 68.57%) evaluation, or low-level data fusion (88.57%, 82.86%). The best SVM model yielded satisfactory performance with 94.29% accuracy for the prediction sets. These results suggested that smartphone imaging coupled with micro-NIR spectroscopy is an effective and low-cost tool for evaluating tea quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)