Jingming Ning

Simultaneous Distillation Extraction of Some Volatile Flavor Components from Pu-erh Tea Samples—Comparison with Steam Distillation-Liquid/Liquid Extraction and Soxhlet Extraction

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.

Safety and anti-hyperglycemic efficacy of various tea types in mice.

Tea, a beverage consumed worldwide, has proven anti-hyperglycemic effects in animal models. Better efficacies of tea beverages are frequently associated with high-dose levels, whose safety attracts considerable attention. Based on the inherent nature of tea catechin oxidation, fresh tea leaves are manufactured into diverse tea types by modulating the oxidation degree of catechins. The present study aimed to assess various tea types for their safety properties and anti-hyperglycemic effects. Mice were allowed free access to tea infusion (1:30, w/v) for one week, and the rare smoked tea caused salient adverse reactions, including hepatic and gastrointestinal toxicities; meanwhile, the widely-consumed green and black teas, unlike the rare yellow tea, suppressed growth in fast-growing healthy mice. When mice were fed a high-fat diet and allowed free access to tea infusion (1:30, w/v) for 25 days, only yellow tea significantly reduced blood glucose. Therefore, various teas showed different safety profiles as well as anti-hyperglycemic efficacy strengths. To achieve an effective and safe anti-hyperglycemic outcome, yellow tea, which effectively suppressed high-fat diet-induced early elevation of hepatic thioredoxin-interacting protein, is an optimal choice.

Back Propagation-Artificial Neural Network Model for Prediction of the Quality of Tea Shoots through Selection of Relevant Near Infrared Spectral Data via Synergy Interval Partial Least Squares

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−1 to 11645 cm−1, 9087.5 cm−1 to 8242.7 cm−1, 8238.9 cm−1 to 7394.2 cm−1, and 6541.7 cm−1 to 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.

Highly selective defluoridation of brick tea infusion by tea waste supported aluminum oxides

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.

Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems: Multisensor data fusion to evaluate green tea quality

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.

Discrimination of six tea categories coming from different origins depending on polyphenols, caffeine, and theanine combined with different discriminant analysis

ABSTRACT 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.

Colorimetric sensor array-based artificial olfactory system for sensing Chinese green tea’s quality: A method of fabrication

ABSTRACT 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.

Corrigendum: Safety and anti-hyperglycemic efficacy of various tea types in mice

Scientific Reports 6: Article number: 31703; published online: 17 August 2016; updated: 08 November 2016 .