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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: Food fraud causes significant economic losses for the industry and generates distrust between the consumers and traders. Tea is one of the most valued beverages throughout the world, being vulnerable to economically motivated cheat. The objective of the study was to develop the potential of hyperspectral imaging (HSI) allying multivariate analysis and data fusion to identify the authenticity of Keemun black tea quality categories. Data fusion that integrated of texture characteristics based on grey level co‐occurrence matrix and visible and near‐infrared spectral features via competitive adaptive reweighted sampling (CARS) was as the target data for modelling. Support vector machine (SVM) and random forest (RF) were utilised for the classification of tea samples of seven grades. The RF model using fused data gave the best performance with the correct discriminant rate of 92.70% for the prediction set. This study demonstrated that HSI coupled with RF was effective in identifying tea sample rank. [ABSTRACT FROM AUTHOR]

Fixation is crucial for green tea quality; previous studies were conducted on finished teas and focused on the volatile metabolites of green tea. However, non-volatile metabolites' (NVMs) evolution and formation mechanisms during fixation remain unclear. Here, fixated tea samples were collected at seven different time points during the green tea fixation stage, the dynamic changes and formation mechanisms of NVMs investigated via ultra-performance liquid chromatography-tandem mass spectrometry and enzymatic activity analyses. The results showed that fixation contained enzymatic activity thermal and non-enzymatic thermal stages. Moreover, 1196 NVMs (10 classes) were detected, and significant NVM variations were observed during the "enzymatic activity thermal" fixation stages, with increasing trends in lipids and amino acids, except alkaloids. Furthermore, 160 common differential NVMs were identified. KEGG analysis revealed that unsaturated fatty acid, linoleic acid phenylpropanoid, and flavonoid metabolism pathways were essential during fixation and influenced green tea aroma, taste, and color formation. These results provide a theoretical basis to regulate green tea fixation technology and enrich the chemical theory of green tea processing. • Fixation contained enzymatic activity thermal and non-enzymatic thermal stages. • Metabolites change dramatically during the enzymatic activity thermal stages. • 160 common differential NVMs were identified in the green tea-fixated samples. • Unsaturated fatty acid and linoleic acid metabolism is crucial for aroma formation. • Phenylpropanoid and flavonoid metabolism is crucial for taste and liquor formation. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

Summary: This study aimed to characterise the volatiles that contribute to stale odour of green tea. Volatiles were extracted using headspace solid‐phase microextraction (HS‐SPME) and analysed using gas chromatography‐mass spectrometry (GC‐MS) and gas chromatography‐olfactometry (GC‐O). Results showed that a total of ninety‐six volatiles were identified by GC‐MS, in which forty‐four volatiles were screened out based on Principal component analysis (PCA) and orthogonal projections to latent structures–discriminant analysis (OPLS‐DA), and thirty‐nine volatiles had a significant variation at the level of 0.05 by analysis of variance (anova). From GC‐O analysis, fifty‐four aromatic volatiles with strong aroma intensity (aroma intensity above 2) were perceived. Further investigation revealed that fifteen volatiles, including 1‐octen‐3‐ol, benzyl alcohol, benzaldehyde, safranal, β‐cyclocitral, (E,E)‐3,5‐octadien‐2‐one, (Z,E)‐3,5‐octadien‐2‐one, dimethyl adipate, dihydroactinidiolide, β‐ionone, α‐ionone, geranyl acetone, phenylethyl alcohol, methyl decanoate and α‐terpineol were responsible for stale odour, besides the former nine compounds were only found in stored tea. [ABSTRACT FROM AUTHOR]

Seventy‐three Lu'an guapian tea (LAGP) samples were collected from 11 growing locations in the city of Lu'an, Anhui Province, China. Through high‐performance liquid chromatography, 18 amino acids, along with gallic acid, caffeine, and five catechins, were quantitatively detected. Hierarchical cluster, correlation and principal component analysis, and a support vector machine were used for geographical discrimination. The findings suggested that the differences in tea quality between the inner and outer mountain regions are related to isoleucine, leucine, phenylalanine, and valine contents, with a correlation coefficient of more than 0.85. Principal component analysis combining with support vector machine was a feasible method. The identification rates for the inner and outer mountains were 97.96% in the training set and 95.83% in the prediction set. Furthermore, the identification rates for the three counties were 91.84% and 95.83% in the training and prediction sets, respectively. [ABSTRACT FROM AUTHOR]

BACKGROUND: The instrumental evaluation of tea quality using digital sensors instead of human panel tests has attracted much attention globally. However, individual sensors do not meet the requirements of discriminant accuracy as a result of incomprehensive sensor information. Considering the major factors in the sensory evaluation of tea, the study integrated multisensor information, including spectral, image and olfaction feature information. RESULTS: To investigate spectral and image information obtained from hyperspectral spectrometers of different bands, principal components analysis was used for dimension reduction and different types of supervised learning algorithms (linear discriminant analysis, K‐nearest neighbour and support vector machine) were selected for comparison. Spectral feature information in the near infrared region and image feature information in the visible‐near infrared/near infrared region achieved greater accuracy for classification. The results indicated that a support vector machine outperformed other methods with respect to multisensor data fusion, which improved the accuracy of evaluating green tea quality compared to using individual sensor data. The overall accuracy of the calibration set increased from 75% using optimal single sensor information to 92% using multisensor information, and the overall accuracy of the prediction set increased from 78% to 92%. CONCLUSION: Overall, it can be concluded that multisensory data accurately identify six grades of tea. © 2018 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Summary: The effects of green tea powder (GTP) on the properties of hard red winter (HRW) flour and Chinese steamed bread were investigated. GTP was blended with HRW flour at levels of 0.00, 1.00, 2.00, 3.00 and 4.00 g GTP/100 g wheat flour. With the addition of GTP, the maximum torque of flour became stronger. The resilience and degree of green colour of steamed bread increased, while hardness and chewiness decreased, with the addition of GTP. The addition of 1.00% GTP did not significantly affect the specific volume, but remarkably influenced the hardness and resilience of steamed bread. In steamed bread formulated with GTP, the antioxidant activity was 0.84 mm TE per g at 1.00% of GTP and increased with GTP levels. The steamed bread had a pleasant flavour, and the degree of overall liking of steamed bread increased with GTP levels to the maximum used in this work. Green tea powder increased the antioxidant activity of steamed bread, and did not affect the quality of steamed bread with a pleasant tea flavour. [ABSTRACT FROM AUTHOR]

Summary: The effects of roasting treatment on sensory quality and related compounds in green tea were investigated. The combined application of roasting temperature and time 100 °C for 90 min, 120 °C for 60 min and 140 °C for 30 min achieved the most satisfying sensory quality. The HPLC analysis showed that the contents of cis‐catechins, bitter and umami amino acids reduced with increase in temperature, while the contents of trans‐catechins and soluble sugar increased with the temperature rise. The content of caffeine and sweet amino acids did not change significantly compared with untreated tea sample. The decrease in cis‐catechins, total polyphenols and bitter amino acids reduced the bitterness and astringency of the green tea, while the increase in total soluble sugars improved the sweetness and mellow taste. The sensory evaluation results showed that the sensory quality of the tea was significantly enhanced through roasting treatment. Roasting treatment significantly improved the sensory quality of green tea. [ABSTRACT FROM AUTHOR]

The effects of green tea powder (GTP) on the quality attributes and antioxidant activity of whole-wheat flour (WWF) pan bread were studied. Crumb hardness, cell diameter, and chewiness of WWF pan bread increased at higher GTP levels, whereas specific volume and brightness showed a reverse trend. At the 1.00 g GTP/100 g flour addition level, hardness, resilience and specific volume of WWF pan bread were not significantly affected, but antioxidant activity increased by 1.00 mM TE/g sample. In the storage test, GTP greatly inhibited the production of peroxide, as determined by peroxide value (PV), in WWF pan bread. After 8 days of storage at room temperature, the PV of the lipid fraction of WWF pan bread without GTP (control) increased by 0.35 mg/100 g, while the PV of bread with the addition of 1.00 g GTP/100 g flour increased by 0.14 mg/100 g. These results suggest that the addition of GTP at the 1.00 g/100 g flour level effectively increases the antioxidant activity, and significantly reduces the production of peroxide during storage, while at the same time maintaining the baking quality of WWF pan bread. [ABSTRACT FROM AUTHOR]

Tea quality is often evaluated by experienced tea tasters; however, their assessments are subjective, being influenced by their individual physiological and psychological condition. Herein, we fabricated a colorimetric sensor array-based artificial olfactory system for sensing the quality of Chinese green tea. First, the colorimetric sensors array was man-made using printing 12 chemically responsive dyes (9 porphyrins, metalloporphyrins and 3 pH indicators) on silica-gel flat plate. The plate was exposed to volatile organic compounds, and the colour changes in each sample were obtained by distinguishing between the images of sensor array before and after contact with tea sample. The values of colour composition changes were extracted from the dyes’ colour sections. Multivariate calibrations were applied through principal component analysis and back propagation artificial neural network (BP-ANN) for modelling. The optimum BP-ANN model was obtained with nine principal components, and the discrimination rate was equal to 85% and 86% in the calibration and prediction sets, respectively. We thus conclude that the low cost colorimetric sensor array-based artificial olfactory technique has great potential for application in intelligent evaluation of the quality of green tea. [ABSTRACT FROM AUTHOR]

Tea polyphenols are important ingredients for evaluating tea quality. The rapid development of sensors provides an efficient method for nondestructive detection of tea polyphenols. Previous studies have shown that features obtained from single or multiple sensors yield better results in detecting interior tea quality. However, due to their lack of external features, it is difficult to meet the general evaluation model for the quality of the interior and exterior of tea. In addition, some features do not fully reflect the sensor signals of tea for several categories. Therefore, a feature fusion method based on time and frequency domains from electronic nose (E-nose) and hyperspectral imagery (HSI) is proposed to estimate the polyphenol content of tea for cross-category evaluation. The random forest and the gradient boosting decision tree (GBDT) are used to evaluate the feature importance to obtain the optimized features. Three models based on different features for cross-category tea (black tea, green tea, and yellow tea) were compared, including grid support vector regression (Grid-SVR), random forest (RF), and extreme gradient boosting (XGBoost). The results show that the accuracy of fusion features based on the time and frequency domain from the electronic nose and hyperspectral image system is higher than that of the features from single sensor. Whether based on all original features or optimized features, the performance of XGBoost is the best among the three regression algorithms (R2 = 0.998, RMSE = 0.434). Results indicate that the proposed method in this study can improve the estimation accuracy of tea polyphenol content for cross-category evaluation, which provides a technical basis for predicting other components of tea. [ABSTRACT FROM AUTHOR]

[Display omitted] • White tea shows green, fresh, floral and fruity odors under solar withering (SW). • SW adds the levels of fatty acid-derived volatiles and volatile terpenoids (VTs). • SW decreases the level of amino acid-derived volatiles (AADVs). • Find out 26 key genes enriched in aroma synthesis pathways under SW. • Large VTs and AADVs with fruity and sweet odors increase with SW duration. Withering is a crucial process that determines the quality of white tea (WT). Solar withering (SW) is reported to contribute to the aroma quality of WT. However, the mechanism by which aroma is formed in WT subjected to SW remains unclear. In this study, through headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC–MS) and transcriptomics, we found that 13 key genes enriched in the mevalonic acid and methylerythritol phosphate pathways, such as those of 1-deoxy- D -xylulose-5-phosphate synthase and terpineol synthase, were significantly upregulated, promoting the accumulation of α -terpinolene, geraniol, and nerolidol, which imparted floral and fruity odors to WT subjected to SW. Additionally, the significant upregulation of lipoxygenases enriched in the lipoxygenase pathway promoting the accumulation of hexanol, 1-octen-3-ol, (E , Z)-3,6-nonadien-1-ol, and nonanal, which contributed to the green and fresh odor in WT subjected to SW. This study provided the first comprehensive insight into the effect mechanism of SW on aroma formation in WT. [ABSTRACT FROM AUTHOR]

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. • Binary mixed models of ionones showed additive interactions on floral enhancement. • Four volatiles showed additive interactions on sweetness taste enhancement. • The sweetness enhancement of OSGT was induced by volatiles, not soluble sugars. • Hydrogen bonding and hydrophobic interactions occur between volatiles and receptors. [ABSTRACT FROM AUTHOR]

[Display omitted] • The effects of processing stages on LAGP aroma constitutions were clarified. • Spreading and deep baking affected the aroma quality of LAGP the most. • Low-temperature spreading (4 °C) could increase the aroma content in tea leaves. • Low-temperature with long baking time was favorable to the formation of tea aroma. 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. [ABSTRACT FROM AUTHOR]

Longjing tea (Camellia sinensis), an iconic agricultural product distinguished by its geographical indication status, has gained much acceptance and consumption in China. The aroma types and steeping conditions of green tea are important factors influencing consumer acceptance. However, the effects of steeping conditions on tea samples with different aroma types are still unknown. Explanation of the above unknowns can provide new insights into the flavor variations of tea when consumed for optimal sensory enjoyment. Here, the aroma profiles of Longjing green teas with bean-like (DX) and fresh aroma (QX) types were analyzed using gas chromatography‒mass spectrometry (GC‒MS), gas chromatography–ion mobility spectrometry (GC‒IMS), and quantitative descriptive analysis (QDA). Geraniol, (E, E)-2,4-heptadienal and other 11 compounds contributed to the bean-like aroma, while heptanal-d, hexanal-d and other 4 compounds contributed to the fresh aroma. Further, the release patterns of volatiles at different steeping temperatures were also revealed. 12 volatiles led to the differences between the two aroma types of Longjing teas at different steeping temperatures. Most volatiles exhibited their aroma characteristics when the steeping temperature exceeded 80 °C. These results enhance the comprehension of green tea's aroma profile, thereby enabling consumers to approach tea drinking from a more scientific perspective. [Display omitted] • Contributing volatiles in different Longjing tea were revealed by GC-MS and GC-IMS. • 18 key volatiles among tea samples were confirmed by multivariate analysis. • Steeping above 80 °C was conducive to the formation of aroma in Longjing tea. • The effect of steeping temperature on 12 key aroma-active compounds was revealed. [ABSTRACT FROM AUTHOR]

[Display omitted] • Over-fired drying can significantly improve the quality of LAGP tea. • LC–MS, HPLC and GC–MS revealed the taste and aroma compounds due to TSD and PLD. • A total of 5 compounds related to taste were selected by DOT > 1. • Concentrations of volatiles were 45.46% higher after PLD treatment. 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. [ABSTRACT FROM AUTHOR]

From the perspective of combating fraud issues and examining keemun black tea properties, there was a contemporary urgent demand for a keemun black tea rankings identification system. Current rapid evaluation systems had been mainly developed for green tea grade evaluation, but there was space for improvement to establish a highly robust model. The present study proposed cognitive spectroscopy that combined near infrared spectroscopy (NIRS) with multivariate calibration and feature variable selection methods. We defined "cognitive spectroscopy" as a protocol that selects characteristic information from complex spectral data and showed optimal results without human intervention. 700 samples representing keemun black tea from seven quality levels were scanned applying an NIR sensor. To differentiate which wavelength variables of the acquired NIRS data carry key and feature information regarding keemun black tea grades, there were four different variables screening approaches, namely genetic algorithm (GA), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and shuffled frog leaping algorithm (SFLA), were compared in this study. Cognitive models were developed using least squares support vector machine (LSSVM), back propagation neural network (BPNN) and random forest (RF) methods combined with the optimized characteristic variables from the above variables selection algorithms for the identification of keemun black tea rank quality. Experimental results showed that all cognitive models utilizing the SFLA approach achieved steady predictive results based on eight latent variables and selected thirteen characteristic wavelength variables. The CARS-LSSVM model with the best predictive performance was proposed based on selecting ten characteristic latent variables, and the best performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0413, the correlation coefficients of prediction set (R p) was 0.9884, and the correct discriminant rate (CDR) was 99.01% in the validation process. This study demonstrated that cognitive spectroscopy represented a proper strategy for the highly identification of quality rankings of keemun black tea. Unlabelled Image • Highly identification of keemun black tea rank based on cognitive spectroscopy. • Feature information were selected by GA, SPA, CARS and SFLA. • Cognitive models based on keemun black tea grades were developed using LSSVM, BPNN and RF. • The SFLA approach achieved steady predictive results. • The CARS-LSSVM model with the best predictive performance was proposed. [ABSTRACT FROM AUTHOR]

• IDA sensor showed high sensitivity for catechin detection. • IDA elements were composed of natural and edible materials. • IDA sensor provided accurate authentication of PDO Longjing tea. • IDA has the advantage of being hazardous-free and cost-efficient. 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. [ABSTRACT FROM AUTHOR]

• Volatile and non-volatile metabolites were characterized and analyzed. • Different data fusion and feature extraction methods were investigated. • Common dimension was used to analyze block weights and visualize data. • Mid-level data fusion in the RBF-SVM model achieved 93.33% test set accuracy. • HS-SPME-GC–MS and NMR achieved synergy to obtain higher classification accuracy. 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 1H 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. [ABSTRACT FROM AUTHOR]

[Display omitted] • Contributing volatiles for HX and LX aromas were decoded by GC–MS and GC–IMS. • Differential volatiles among HX, LX, QX were confirmed by VIP of PLS-DA and relative OAV. • Alcohols were the main contributing volatiles for floral-like aroma in TH green teas. 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. [ABSTRACT FROM AUTHOR]

[Display omitted] • IDA sensor showed high sensitivity for EGCG detection. • IDA elements were composed of low-cost accessible pH dyes and metal ions. • IDA provided qualitative discrimination of green tea freshness. • IDA provided quantitative prediction of catechins in green tea during storage. 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. [ABSTRACT FROM AUTHOR]

Fixation is the key step of green tea processing, usually with moisture content as the quality evaluation indicator. Near-infrared (NIR) spectroscopy has been widely used in tea moisture detection but is mostly realized in the laboratory, and online detection in tea processing is limited. This study explores the possibility of calibration transfer from lab-based hyperspectral imaging (HSI) to online NIR to enable the rapid detection of moisture during the fixation step. Raw spectral data were preprocessed, and direct standardization (DS) and partial least-squares regression (PLS) were used for data calibration and modeling, respectively. The results showed that there were observable differences in the spectral data of the same sample obtained by different instruments. The prediction model based on HSI failed to be transferred directly to the online NIR and has a large root mean square error of prediction (RMSEP) value. Spectral preprocessing with a standard normal variate (SNV) transformation reduced the differences between the instruments. When 40 standard samples were used, the established transferable SNV-DS-PLS model based on HSI achieved the optimal prediction performance with a correlation coefficient of prediction (Rp), RMSEP, and ratio of prediction to deviation (RPD) of 0.94, 2.24%, and 2.76, respectively, for the 50 validated tea samples obtained from online NIR. In conclusion, the model transfer across instruments was successfully realized, and tea moisture detection was brought from the laboratory to the tea factory, which provided a reference model for online moisture detection for other agricultural products. • Calibration transfer from HSI to online NIR for tea processing was optimized. • Online detection of moisture during green tea fixation was achieved. • Model built based on HSI fails to be transferred directly to the online NIR. • Transferable SNV-DS-PLS model gave the optimal performance for moisture. [ABSTRACT FROM AUTHOR]

[Display omitted] • Strip green tea (SGT) volatile metabolite variations during process were clarified. • Fixation and rubbing were the key processes responsible for SGT aroma formation. • Lipid and glycoside derivatization were crucial in the volatile conversion of SGT. • Thirty-four differential volatiles were identified at different rubbing degrees. • Moderate rubbing was beneficial to the shape and aroma formation of SGT. 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. [ABSTRACT FROM AUTHOR]

[Display omitted] • The yellowing process improves the flavor of SGT. • Yellowing significantly decreases gallated catechins. • Benzene acetaldehyde and β-ionone enhance the sweetness of SGT. • 1-octen-3-ol enhances the umami of SGT. 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. [ABSTRACT FROM AUTHOR]

[Display omitted] • NIR-HSI coupled with chemometrics was used to identify green tea. • Both the origin and processing month of green tea samples were identified. • The accuracy of the discriminant model for origin identification reached 97.5%. • The model discriminated the processing month with an accuracy of 95%. • Fast and non-destructive methods for green tea identifications were established. 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. [ABSTRACT FROM AUTHOR]

Withering, or "spreading" in Chinese word, is a key process in Chinese green tea production. However, the underlying mechanisms of this treatment remains unclear. To investigate the effect of withering treatment on the quality of postharvest tea leaves, UHPLC-Orbitrap-MS metabolomics and tandem mass tag (TMT)-based proteomics analysis were performed. A total of 69 metabolites were identified, and up to 136 different expressed proteins were obtained through pair-wise comparisons. Comprehensive metabolomics and proteomics analyses revealed a monotonic metabolic shift ascribed to the tea leaves' response to dehydration stress caused by harvesting. Multiple amino acids were increased in level due to protein degradation; the biosynthesis of catechins and flavonoids were inhibited; the level of glycosidically bound volatiles were decreased attributed to the metabolism enhancement. These metabolic changes provide a material basis for later processes in green tea production and help with the quality formation of green tea. • Metabolomics and proteomics were used for tea withering study. • A comprehensive view was acquired by combining metabolomics and proteomics results. • Amino acids and glycosidically bound volatiles were mostly affected during withering. • Withered leaves present a monotonic metabolic shift ascribed to multiple stresses. [ABSTRACT FROM AUTHOR]

• Smartphone-based micro NIRS was used to collect spectrum of green tea samples. • A multilayer algorithmic model was designed to explain the obtained spectral data. • Simplified models were established by using three wavelength selection algorithms. • A low-cost and in-situ method was proposed to detect adulteration in green tea. 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. [ABSTRACT FROM AUTHOR]

Tea storage period is difficult to be assessed by traditional sensory and chemical analysis methods. In this research, hyperspectral imaging (HSI) covering the spectral range of 400–1000 nm was combined with chemometric methods to predict the storage period of green tea samples. Tea samples were incubated under two storage temperatures of 4 °C (Sample set 1) and 25 °C (Sample set 2) for 360 days. The reflectance spectra were acquired every 90 days. Based on the preprocessed full spectra, partial least squares-discriminant analysis (PLS-DA)- and support vector machine (SVM)-based models had acceptable outputs for the two sample sets with an accuracy of over 90%. In addition, regression coefficients (RCs) of PLS-based models, second derivative (2-Der), and successive projections algorithm (SPA) were applied to select the optimal wavelengths. Satisfactory results were achieved by SVM-based models on the basis of optimal wavelengths. The SPA-SVM- and 2-Der-SVM-based models had the best performance to discriminate the storage period of the two sample sets with the prediction accuracies of 98% and 98%, respectively. The results demonstrated that HSI could be used to evaluate the storage period of green tea, providing an alternative for a real-time monitoring of tea quality. [ABSTRACT FROM AUTHOR]

Caffeine and catechin are two main components of instant green tea, and are essential components of tea quality. This paper mainly focuses on the feasibility of rapidly determining instant green tea components by using a portable near infrared (NIR) spectrometer. The two main components (caffeine and catechin) were studied. In addition, the instrument performance levels of portable and benchtop NIR spectrometers were studied and compared. Quantitative models developed using portable and benchtop spectrometers for measuring caffeine, total catechins, and four individual catechins were established and compared. After preprocessing using standard normal variate (SNV), the Rp values of the caffeine, total catechins, (−)-epigallocatechin, (−)-epigallocatechin 3-gallate, (−)-epicatechin, and (−)-epicatechin gallate in the partial least squares models for a portable NIR spectrometer were 0.974, 0.962, 0.669, 0.945, 0.942 and 0.905, respectively. For a benchtop NIR spectrometer, Rp values were 0.993, 0.958, 0.883, 0.955, 0.966 and 0.936, respectively. Passing-Bablok regression method results indicated no significant differences between the two instruments. A genetic algorithm (GA) and the successive projections algorithm (SPA) were used to screen the wavelength of the NIR spectrum and establish the model. The GA obtained more robust modeling results. This study concludes that the developed portable spectroscopy system combined with appropriate variable selection methods can be effectively used for rapid determination of caffeine, total catechins, and four individual catechins in instant green tea. Unlabelled Image • Established a rapid and nondestructive testing method for instant green tea • Evaluated feasibility of the portable NIR spectrometer compared to benchtop instruments • Optimized modeling process by applying spectral pretreatment and variable selection • Provided a novel method for the mass production and quality control of instant green tea [ABSTRACT FROM AUTHOR]