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Ripening is a key process driving the transformation of non-volatiles in Keemun congou black tea (KCBT), affecting its flavour profile and health functions. In this study, taste was quantitatively evaluated by using sensory and biomimetic electrodes and by employing metabolomic techniques. The results revealed that the content of polyphenols was greatly affected by ripening, catechins and flavonoids reduced by 63.5 % and 9.2 %, respectively, and theaflavins increased by 14.6 %, thereby attenuating the bitterness and astringency of the tea infusion while enhancing its sweetness and mellowness. Experiments regarding the inhibition of α-amylase and α-glucosidase activity and scavenging of 2,2-diphenyl-1-picrylhydrazyl radical revealed that ripening triggered the cascade reaction of polyphenols to form catechin polymers and flavonoid glycosides, thereby changing the dual biological functions of hypoglycaemia and free radical scavenging in vitro. Our study confirms the key role of ripening in enhancing the taste quality and potential health functional activities of KCBT.

Drying technology plays a pivotal role in tea processing. Herein, the differences in color, non-volatile, and volatile components of green tea under various drying methods were investigated. The results indicated that vacuum freeze-microwave increased the L* and b* values, and decreased the a* values of tea leaves. Moreover, vacuum freeze-microwave drying resulted in higher polyphenol content than the other three drying methods although there was no significant difference. A total of 43 volatile compounds were identified. Of these, 2-propanone, ethanol(D), ethanol(M), ethyl acetate(M), 2-methyl-1-butanol, and 2-methylthiophene were found to play an important role in the above discrimination (VIP >1.5). Dry extraction showed a higher content of volatile components than wet extraction. Regardless of the extraction conditions, vacuum freeze-microwave drying exhibited a stronger signal intensity and more volatile components than other drying methods. This study provides a reference for analyzing the quality differences of green tea by different drying methods.

White tea is a naturally processed type of tea that has a unique favorable aroma. Typically, the aroma of white tea depends on its origin. Compared with Fujian white tea (FJ) and Yunnan other origin white tea (YO), Jinggu white tea (JG) has a stronger fruity and sweet aroma. In this study, to determine the factors underlying the unique fruity and sweet aroma of JG, we used YO and FJ as control samples and analysed the samples by using a molecular sensory science technique. Olfactory experiments and odor activity analysis revealed 10 key active substances to contribute to the aroma of JG. Aroma addition experiments further showed that linalool and benzeneacetaldehyde were the main contributors to the fruity and sweet aroma of JG, respectively. The results are helpful to understand the aroma of JG and provide a theoretical basis for the quality control of JG.

In this study, the stems (ST) and leaves (LT) isolated from Large-leaf yellow tea (LYT) were used for sensory evaluation and quantitative analysis of flavor metabolites by sensomics and flavoromics. The results showed that the flavors of ST and LT in LYT were significantly different, and ST had stronger roasty and nutty aroma and sweet taste, which was mainly due to the accumulation of higher theanine and soluble monosaccharides in ST, and provided more substrates for the production of more pyrazine by the Maillard reaction; whereas LT contributed to the mellow and thick taste quality of LYT, and the abundance of catechins and caffeine were the main reason. The metabolic patterns of flavor metabolites indicated that the flavor differences between ST and LT were mainly due to biological metabolism in tea plants. This study provides the selection of raw materials for LYT in the future and product development of tea stems.

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.

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.

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.

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.

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.

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.

Drying greatly affects the aroma of black tea. In this study, the differences in aroma of black tea under hot-air drying (HD), sun drying (SD), and pan-fired drying (PD) were investigated through quantitative descriptive analysis. Headspace solid-phase microextraction and solvent assisted flavor evaporation combined with gas chromatography–mass spectrometry and gas chromatography-olfactory were used to analyze the overall aroma profile of black tea. Aroma extract dilution analysis and odor activity values revealed that 15 aroma-active compounds led to differences in aroma, namely linalool, geraniol, phenylethyl alcohol, phenylacetaldehyde, (Z) -linalool oxide (furanoid), β-damascenone, dimethyl sulfide, methional, 2-methylbutanal, 3-methylbutanal, methyl salicylate, β-myrcene, hexanal, 1-octen-3-ol, and heptanal. Among them, geraniol, linalool, and methional significantly enhanced the floral and roasty aroma of HD, while hexanal enhanced the green aroma of SD. Finally, our results were validated through aroma recombination and addition experiments. This study provides a theoretical basis for improving the aroma of black tea.

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.

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.

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.

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.

In order to explore the differences of flavor characteristics among different cultivars (strains) of large-leaf yellow tea, the main tea tree varieties (strains) in Hubei Province were used as materials to process large-leaf yellow tea. The differences in sensory quality, taste and aroma components were analyzed, and chemometrics analysis was used to explore the relationship between taste-characteristic attributes and chemical compositions. The results showed that large-leaf yellow tea of B6-1 and 07-7-44 strains had the best total sensory scores with long-lasting roasting aroma and sweet floral fragrance, the large-leaf yellow tea of D12 and B6-1 strains had the higher contents of non-ester catechins and amino acids, while large-leaf yellow tea of D12, B6-1 and 07-7-44 strains had the lower ratio of tea polyphenols/amino acids, showed the characteristics of mellow and sweet aftertaste. Correlation analysis showed that taste score and total sensory score were significantly positively correlated with the contents of water extracts, amino acids and soluble sugars (P

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.

Accurately distinguishing the types of tea is of great significance to the pricing, production, and processing of tea. The similarity of the internal spectral characteristics and appearance characteristics of different types of tea greatly limits further research on tea identification. However, wavelet transform can simultaneously extract time domain and frequency domain features, which is a powerful tool in the field of image signal processing. To address this gap, a method for tea recognition based on a lightweight convolutional neural network and support vector machine (L-CNN-SVM) was proposed, aiming to realize tea recognition using wavelet feature figures generated by wavelet time-frequency signal decomposition and reconstruction. Firstly, the redundant discrete wavelet transform was used to decompose the wavelet components of the hyperspectral images of the three teas (black tea, green tea, and yellow tea), which were used to construct the datasets. Secondly, improve the lightweight CNN model to generate a tea recognition model. Finally, compare and evaluate the recognition results of different models. The results demonstrated that the results of tea recognition based on the L-CNN-SVM method outperformed MobileNet v2+RF, MobileNet v2+KNN, MobileNet v2+AdaBoost, AlexNet, and MobileNet v2. For the recognition results of the three teas using reconstruction of wavelet components LL + HL + LH, the overall accuracy rate reached 98.7%, which was 4.7%, 3.4%, 1.4%, and 2.0% higher than that of LH + HL + HH, LL + HH + HH, LL + LL + HH, and LL + LL + LL. This research can provide new inspiration and technical support for grade and quality assessment of cross-category tea.

Polyphenols are one of the important ingredients determining the quality of tea, which play an important role in affecting tea quality standards and quality control. At present, NIR spectroscopy technology has been widely used in tea quality detection to achieve good results. However, due to the lack of spatial features, it is difficult to meet the accuracy requirements to comprehensively judge the quality of both inside and outside of tea. With the development of hyperspectral imaging system (HIS), although progress has been made in the estimation of tea texture based on gray level co-occurrence matrix (GLCM) extraction, there are still some obstacles in practical applications. When the resolution is low, the texture features of the image are not significantly different, and a small number of features cannot more fully represent the image features, resulting in lower model accuracy. When the resolution is higher, the increase in the dimension of the feature leads to a more complex model. Therefore, it is necessary to extract multi-scale features from the hyperspectral image while preserving the original information of the hyperspectral image. In this study, a multi-feature fusion method is proposed to improve the accuracy of tea polyphenols content estimation. The process includes multi-scale wavelet decomposition, and feature fusion based on wavelet coefficient features, GLCM texture features and wavelet texture features. Experiments were carried out on five kinds of yellow tea by comparing different models based on different features, including partial least squares regression (PLSR) and support vector regression (SVR). Results indicated that the model based on multi-feature fusion is more accurate than the individual indicators, and models based on SVR perform better than PLSR.

A simutaneous distillation extraction (SDE) combined GC method was constructed for determination of volatile flavor components in Pu-erh tea samples. Dichloromethane and ethyl decylate was employed as organic phase in SDE and internal standard in determination, respectively. Weakly polar DB-5 column was used to separate the volatile flavor components in GC, 10 of the components were quantitatively analyzed, and further confirmed by GC-MS. The recovery covered from 66.4%–109%, and repeatability expressed as RSD was in range of 1.44%–12.6%. SDE was most suitable for the extraction of the anlytes by comparing with steam distillation-liquid/liquid extraction and Soxhlet extraction. Commercially available Pu-erh tea samples, including Pu-erh raw tea and ripe tea, were analyzed by the constructed method. the high-volatile components, such as benzyl alcohol, linalool oxide, and linalool, were greatly rich in Pu-erh raw teas, while the contents of 1,2,3-Trimethoxylbenzene and 1,2,4-Trimethoxylbenzene were much high in Pu-erh ripe teas.