Search

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. [ABSTRACT FROM AUTHOR]

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]

It is of great significance for the production and popularization of yellow tea with standard grade difference. In this paper, 9 yellow tea samples of 3 categories (yellow bud tea, small-leaf yellow tea, large-leaf yellow tea) from western Anhui province were selected. Then 80 consumers and 22 assessors were recruited to analyze these samples using ranking and Napping-UFP respectively. The results show that the 8 standard samples can be distinguished according to the appearance by ranking, except the super class and the first class of bud tea. In Napping-UFP, 22 assessors could distinguish different grades of samples by appearance. The categories (yellow bud tea, small-leaf yellow tea and large-leaf yellow tea) and the roasting degrees (light fire and sufficient fire) could be accurately distinguished based on their aroma or taste. At the same time, the assessors provided relatively rich descriptive terms (33 of appearance, 21 of aroma and 24 of taste), including "green", "yellow" and "fairly stalk" in the appearance attributes, "tend aroma", "sweet aroma" and "tobacco" in the aroma attributes, and "fresh" in the taste attributes. These terms could be used to accurately assess yellow tea from western Anhui province, which is conducive to consumers' understanding and acceptance, as well as the market promotion of yellow tea from western Anhui province. [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]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [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]

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]

The current method for processing bud yellow tea usually results in astringent taste that is not sufficiently yellowing. Therefore, two factors in the production phase that affect tea quality were studied: number of times the tea leaves undergo the yellowing process and predrying of leaves to reduce their water content before a second yellowing process. We then evaluated the colour and nonvolatile compounds of the produced tea. Results revealed that gallated catechins and the polyphenol–amino acid ratio decreased significantly, and the colour of the tea changed from green to yellow after two yellowing processes. Predrying leaves to a lower water content resulted in the accumulation of free amino acids and soluble sugars, and an increase in the brightness and yellow colour of tea infusion. Bud yellow tea with the highest taste evaluation score and yellowness was obtained when the leave samples underwent two yellowing processes and the water content in the leaves was 40%. [ABSTRACT FROM AUTHOR]

BACKGROUND Grading represents an essential criterion for the quality assurance of black tea. The main objectives of the study were to develop a highly robust model for Chinese black tea of seven grades based on cognitive spectroscopy. RESULTS: Cognitive spectroscopy was proposed to combine near‐infrared spectroscopy (NIRS) with machine learning and evolutionary algorithms, selected feature information from complex spectral data and show the best results without human intervention. The NIRS measuring system was used to obtain the spectra of Chinese black tea samples of seven grades. The spectra acquired were preprocessed by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and minimum/maximum normalization (MIN/MAX), and the optimal pretreating method was implemented using principal component analysis combined with linear discriminant analysis algorithm. Three feature selection evolutionary algorithms, which were a genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), were compared to search the best preprocessed characteristic wavelengths. Cognitive models of Chinese black tea ranks were constructed using extreme learning machine (ELM), K‐nearest neighbor (KNN) and support vector machine (SVM) methods based on the selected characteristic variables. Experimental results revealed that the PSO–SVM model showed the best predictive performance with the correlation coefficients of prediction set (Rp) of 0.9838, the root mean square error of prediction (RMSEP) of 0.0246, and the correct discriminant rate (CDR) of 98.70%. The extracted feature wavelengths were only occupying 0.18% of the origin. CONCLUSION: The overall results demonstrated that cognitive spectroscopy could be utilized as a rapid strategy to identify Chinese black tea grades. © 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]

The evaluation of Chinese dianhong black tea (CDBT) grades was an important indicator to ensure its quality. A handheld spectroscopy system combined with chemometrics was utilized to assess CDBT from eight grades. Both variables selection methods, namely genetic algorithm (GA) and successive projections algorithm (SPA), were employed to acquire the feature variables of each sample spectrum. A partial least‐squares discriminant analysis (PLS‐DA) and support vector machine (SVM) algorithms were applied for the establishment of the grading discrimination models based on near‐infrared spectroscopy (NIRS). Comparisons of the portable and benchtop NIRS systems were implemented to obtain the optimal discriminant models. Experimental results showed that GA‐SVM models by the handheld sensors yielded the best predictive performance with the correct discriminant rate (CDR) of 98.75% and 100% in the training set and prediction set, respectively. This study demonstrated that the handheld system combined with a suitable chemometric and feature information selection method could successfully be used for the rapid and efficient discrimination of CDBT rankings. It was promising to establish a specific economical portable NIRS sensor for in situ quality assurance of CDBT grades. [ABSTRACT FROM AUTHOR]

BACKGROUND: Non‐volatile compounds play a key role in the quality and price of Keemun black tea (KBT). The non‐volatile compounds in KBT samples from different producing areas normally vary greatly. The development of rapid methods for tracing the geographical origin of KBT is useful. In this study, we develop models for the discrimination of KBT's geographical origin based on non‐volatile compounds. RESULTS: Seventy‐two KBT samples were collected from five towns in Anhui province to determine 13 KBT compounds by high‐performance liquid chromatography (HPLC). Analysis of variance showed that the content of 13 compounds in KBT indicated significant differences (P < 0.05) among five towns. Three multivariate statistical models including principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), and linear discriminant analysis (LDA) were built to discriminate origin. Principal component analysis effectively extracted three principal components, namely theaflavins, galloylated catechins, and simple catechins. The high sensitivity (64.5%–99.2%) was achieved of SIMCA model. To establish the discriminant functions, six variables (gallic acid, (+)‐catechin, (−)‐epigallocatechin gallate, theaflavin‐3‐gallate, theaflavin‐3,3′‐di‐gallate, and total theaflavins) were chosen from 13 variables, and LDA was applied. This gave a satisfactory overall correct classification rate (94.4%) and cross‐validation rate (88.9%) for KBT samples. CONCLUSION: The results showed that HPLC analysis together with chemometrics is a reliable approach for tracing KBT and guaranteeing its authenticity. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

To identify the microorganisms responsible for the formation of the main quality components of Qingzhuan brick tea (QZBT) during solid-state fermentation (SSF), predominant thermoduric strains were isolated from the tea leaves collected during SSF. According to their capability of releasing cellulase, pectase, protease, and polyphenol oxidase, four strains were selected as starter cultures to ferment sun-dried tea leaves during artificially inoculated SSF. According to the major enzymatic activities and quality components content (tea polyphenols, catechins, amino acids, soluble sugar, and theabrownin), it was found that Aspergillus fumigatus M1 had a significant effect on the transformation of polyphenols and Bacillus subtilis X4 could enhance the ability of bioconversion of strain M1. Strain X4 and M1 may be the core microbes responsible for developing these biochemical components of QZBT, as the values of quality components of tea leaves fermented by these two strains for 6 days was very close to that of the sample naturally fermented for 35 days in the tea factory. The results could be significant in developing industrial starters for the manufacture of QZBT and stabilizing the product quality of different batches. [ABSTRACT FROM AUTHOR]

BACKGROUND Keemun black tea (KBT) is one of the most popular tea beverages in China as a result of its unique flavor and potential health benefits. The geographical origin of KBT influences its quality and price. The present study aimed to apply a head‐space solid phase microextraction approach and gas chromatography–mass spectrometry combined with chemometric analysis to profile the volatile compounds of KBT collected from five production areas. RESULTS: Thirty‐one peaks were detected in 61 KBT samples. Hierarchical cluster analysis, principal component analysis (PCA), k‐nearest neighbor (k‐NN) and stepwise linear discriminant analysis (SLDA) were employed to visualize the volatile fractions. The results of unsupervised statistical tools were compared using a test for similarities and distinctions, which showed that different sources may be associated. A satisfying combination of average recognition (91.7%) and cross‐validation prediction abilities (84.6%) was obtained for the PCA–k‐NN. Among all of the statistical tools, SLDA provided promising results, with 100% recognition and 96.4% prediction ability. CONCLUSION: The results obtained in the present study indicate that the volatile compounds can be used as indicators to identify the geographical origin of KBT. © 2019 Society of Chemical Industry [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: Photosynthetic pigments perform critical physiological functions in tea plants. Their content is an essential indicator of photosynthetic efficiency and nutritional status. The present study aimed to predict chlorophyll a (Chl a), chlorophyll b (Chl b), total chlorophyll (total Chl), and carotenoid (Car) content in tea leaves under different levels of nitrogen treatment using hyperspectral imaging (HSI) in combination with variable selection algorithms. RESULTS: A total of 150 samples were collected and scanned using the HSI system. The mean spectrum in the region of interest (ROI) was extracted, and the pigment content was measured by traditional chemical methods. Five and seven optimal wavelengths (OWs) were selected using the regression coefficients (RCs) of partial least squares regression (PLSR) and the second‐derivative (2‐Der), respectively. The optimal 2‐Der‐PLSR models for Chl a, Chl b, total Chl, and Car performed remarkably well based on seven OWs with correlation coefficients of prediction (RP) of 0.9337, 0.9322, 0.9333 and 0.9036, root mean square errors in prediction (RMSEP) of 0.1100, 0.0511, 0.1620, and 0.0300 mg g‐1, respectively. CONCLUSION: The results of this study revealed that HSI combined with variable selection method can be employed as a rapid and accurate method for predicting the content of pigments in tea plants. © 2018 Society of Chemical Industry [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]

We describe a novel analytical method for quantification of free amino acids in tea using variable mobile phase pH, elution gradient and column temperature of reversed-phase high-performance liquid chromatography (RP-HPLC). The study of mobile phase pH 5.7 was chosen to simultaneous quantification of 19 free amino acids in tea, while it improved maximum resolution of glutamine, histidine and theanine. Elution gradient was adapted for enhancing the solution of free amino acids, mainly because of adjustment of mobile phase A and B. The column temperature of 40 °C was conducive to separate free amino acids in tea. The limit of detection (LOD) and limit of quantitation (LOQ) of this method were in the range of 0.097-0.228 nmol/mL and 0.323-0.761 nmol/mL, respectively. The relative standard deviation of intraday and interday ranged in 0.099-1.909% and 3.231-7.025%, respectively, indicating that the method was reproducible and precise, while recovery ranged between 81.06-112.78%, showing that the method had an acceptable accuracy. This method was applied for the quantification of free amino acids in six types of tea. Multivariate analysis identified serine, glutamine, theanine and leucine as the most influencing factor for classify among analyzed sample. [ABSTRACT FROM AUTHOR]

Abstract: BACKGROUND: Nitrogen (N) fertilizer plays an important role in tea plantation management, with significant impacts on the photosynthetic capacity, productivity and nutrition status of tea plants. The present study aimed to establish a method for the discrimination of N fertilizer levels using hyperspectral imaging technique. RESULTS: Spectral data were extracted from the region of interest, followed by the first derivative to reduce background noise. Five optimal wavelengths were selected by principal component analysis. Texture features were extracted from the images at optimal wavelengths by gray‐level gradient co‐occurrence matrix. Support vector machine (SVM) and extreme learning machine were used to build classification models based on spectral data, optimal wavelengths, texture features and data fusion, respectively. The SVM model using fused data gave the best performance with highest correct classification rate of 100% for prediction set. CONCLUSION: The overall results indicated that visible and near‐infrared hyperspectral imaging combined with SVM were effective in discriminating N fertilizer levels of tea plants. © 2018 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

BACKGROUND Brick tea usually contains very high fluoride, which may affect human health. Biosorbents have received much attention for selective removal of fluoride because of low cost, environmental friendliness, and relative safeness. RESULTS In the present study, a highly selective fluoride tea waste based biosorbent, namely, aluminum (Al) oxide decorated tea waste (Tea-Al), was successfully prepared. The Tea-Al biosorbent was characterized by energy-dispersive spectrometry, Fourier transform infrared spectroscopy, powder X-ray diffraction and X-ray photoelectron spectroscopic analysis. The Tea-Al sample exhibited remarkably selective adsorption for fluoride (52.90%), but a weaker adsorption for other major constituents of brick tea infusion, such as catechins, polyphenols and caffeine, under the same conditions. Fluoride adsorption by Tea-Al for different times obeyed the surface reaction and adsorption isotherms fit the Freundlich model. In addition, the fluoride adsorption mechanism appeared to be an ion exchange between hydroxyl and fluoride ions. CONCLUSION Results from this study demonstrated that Tea-Al is a promising biosorbent useful for the removal of fluoride in brick tea infusion. © 2016 Society of Chemical Industry [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]

This study reported a quantitative method to discriminate six tea categories of 664 tea samples. The main components of tea including gallic acid (GA), caffeine, theanine, (−)-epigallocatechin gallate (EGCG), (−)-epigallocatechin (EGC), (−)-epicatechin gallate (ECG), (−)-epicatechin (EC), and (+)-catechin (C) were determined using high-performance liquid chromatography in accordance with the ISO detection standards. Genetic algorithm and stepwise discriminant method were used for factors selection based on nine indicators (GA, EGC, C, EC, EGCG, ECG, total catechins (TC), caffeine, and theanine). The results of factors selection were first optimized by combining improved indicators; subsequently, Bayesian discriminant and distance discriminant analyses were applied to discriminate tea categories. The results indicated that GA, EGC, caffeine, EGCG, EC, TC, theanine, EGC^1.25, and caffeine^2 combined with Bayesian discriminant analysis provide a feasible method of classifying six tea categories. The total identification rates were 94.13% in the training set and 92.31% in the prediction set. In addition, a satisfactory result was obtained for the discrimination of each tea category. [ABSTRACT FROM AUTHOR]

A total 206 samples of green, yellow, white, black, and Oolong teas were utilized to acquire hyperspectral imaging, and five tea categories were identified based on visible and near-infrared (NIR) hyperspectral imaging, combined with classification pattern recognition. The characteristic spectra were extracted from the region of interest (ROI), and the standard normal variate (SNV) method was preprocessed to reduce background noise. Four dominant wavelengths (589, 635, 670, and 783 nm) were selected by principal component analysis (PCA) as spectral features. Textural features were extracted by the Grey-level co-occurrence matrix (GLCM) from images at selected dominant wavelengths. Linear discriminant analysis (LDA), library support vector machine (Lib-SVM), and extreme learning machine (ELM) classification models were established based on full spectra, spectral features, textural features, and data fusion, respectively. Lib-SVM was the best model with the input data fusion, and the correct classification rate (CCR) achieved 98.39%. The results implied that visible and NIR hyperspectral imaging combined with Lib-SVM has the capability of rapidly and non-destructively classifying tea categories. [ABSTRACT FROM AUTHOR]

Six tea types were stepwise discriminated based on their catechins, caffeine, and theanine contents of total 436 tea samples collected worldwide, combined with Fisher classification pattern recognition. Those tea samples of six types (green, white, yellow, oolong, black, and dark teas) of commonly consumed teas with different processing methods were analyzed in this work. Five main catechins ((−)-epigallocatechin gallate (EGCG), (−)-epigallocatechin (EGC), (−)-epicatechin gallate (ECG), (−)-epicatechin (EC), and (+)-catechin (C)), caffeine, and theanine contents were accurately measured by high-performance liquid chromatography (HPLC). As a novel approach, stepwise identification combined with Fisher discriminant analysis was applied to develop an identification model. Several parameters, including model component factors, were optimized by cross-validation. The optimal Fisher model was achieved with caffeine, total catechins, theanine, theanine × theanine, EGCG/total catechins, and theanine × caffeine as component factors. The discrimination rates of black, dark, white, oolong, yellow, and green teas were 95.90, 100.00, 97.40, 95.70, 91.80, and 88.30 %, respectively. Compared with other pattern recognition approaches, the Fisher algorithm exhibited excellent performance in the final identification. The overall results show that this method is suitable to stepwise identify six tea categories, according to the measurements of main chemicals with catechins, caffeine, and theanine by HPLC and followed by the Fisher pattern recognition. [ABSTRACT FROM AUTHOR]

Near-infrared spectroscopy and back propagation-artificial neural network (BP-ANN) model in conjunction with synergy interval partial least squares (siPLS) algorithm were used to evaluate tea shoots quality. The near-infrared spectra regions relevant to tea quality (12493 cm−1to 11645 cm−1, 9087.5 cm−1to 8242.7 cm−1, 8238.9 cm−1to 7394.2 cm−1, and 6541.7 cm−1to 5697 cm−1) were selected using siPLS algorithm. The two principal components that explained 99.46% of the variability in this spectral data were then used to calibrate the BP-ANN quality index (QI) model. The performance of this model [the coefficient of determination for prediction (), 0.9680; root mean square error of prediction (RMSEP), 0.0178] was superior to those of the BP-ANN model (= 0.9332, RMSEP = 0.0285) and the siPLS model (= 0.9230, RMSEP = 0.0360). The predicted QI values of 25 samples highly correlated with the experimental values (= 0.9223, RMSEP = 0.0344). The QI model with the combined siPLS-BP-ANN algorithms accurately predicted the quality of tea shoots. [ABSTRACT FROM PUBLISHER]

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

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]