The Effects of Structure and Oxidative Polymerization on Antioxidant Activity of Catechins and Polymers.

Polyphenols are key free radical scavengers in tea. This study screened the antioxidant active groups of catechins and dimers and analyzed the effects of the degree of oxidative polymerization and oxidative dimerization reaction on their antioxidant activities. ABTS+· free radical scavenging activity, DPPH free radical scavenging activity, and total antioxidant capacity of catechins and polymers were systematically analyzed and compared in this study. Results manifested antioxidant activities of catechins were dominated by B-ring pyrogallol and 3-galloyl, but were not decided by geometrical isomerism. 3-galloyl had a stronger antioxidant activity than B-ring pyrogallol in catechins. The number, not the position, of the galloyl group was positively correlated with the antioxidant activities of theaflavins. Theasinensin A has more active groups than (-)-epigallocatechin gallate and theaflavin-3,3'-digallate, so it had a stronger antioxidant activity. Additionally, the higher the degree of oxidation polymerization, the weaker the antioxidant activities of the samples. The oxidative dimerization reaction hindered the antioxidant activities of the substrate-catechin mixture by reducing the number of active groups of the substrate and increasing the molecular structure size of the product. Overall, pyrogallol and galloyl groups were antioxidant active groups. The degree of oxidative polymerization and the oxidative dimerization reaction weakened the antioxidant activity.

Bitterness quantification and simulated taste mechanism of theasinensin A from tea.

Theasinensin A is an important quality chemical component in tea, but its taste characteristics and the related mechanism are still unclear. The bitterness quantification and simulated taste mechanism of theasinensin A were researched. The results showed that theasinensin A was significantly correlated with the bitterness of tea. The bitterness threshold of theasinensin A was identified as 65 µmol/L for the first time. The dose-over-threshold (DOT) value of theasinensin A was significantly higher than that of caffeine in black tea soup. The concentration-bitterness curve and time-intensity curve of theasinensin A were constructed. The bitterness contribution of theasinensin A in black tea was higher than in oolong and green tea. Theasinensin A had the highest affinity with bitterness receptor protein TAS2R16, which was compared to TAS2R13 and TAS2R14. Theasinensin A was mainly bound to a half-open cavity at the N-terminal of TAS2R13, TAS2R14, and TAS2R16. The different binding capacity, hydrogen bond, and hydrophobic accumulation effect of theasinensin A and bitterness receptor proteins might be the reason why theasinensin A presented different bitterness senses in human oral cavity.

A new catechin oxidation product and polymeric polyphenols of post-fermented tea.

A new epicatechin oxidation product with a 3,6-dihydro-6-oxo-2H-pyran-2-carboxylic acid moiety was isolated from a commercially available post-fermented tea that is produced by microbial fermentation of green tea. The structure of this product was determined by spectroscopic methods. A production mechanism that includes the oxygenative cleavage of the catechol B-ring of (-)-epicatechin is proposed. In addition, polymeric polyphenols were separated from the post-fermented tea and partially characterised by (13)C NMR spectroscopy and gel-permeation chromatography. The polymers appear to be primarily composed of epigalloacetechin-3-O-gallate and the molecular weight (Mn) of the acetylated form was estimated to be ∼3500.

[Studies on ANN models of determination of tea polyphenol and amylose in tea by near-infrared spectroscopy].

The objectives of the present paper were to build the models for the determination of tea polyphenol (TP) and tea amylose (TA) in tea by near-infrared spectroscopy (NIR). According to the range of 7432.3-6155.7 cm(-1) and 5484.6-4192.5 cm(-1) of NIR spectra, the models are built for determining the contents of TP and TA in tea with the input layer, hidden layer and node ((8, 4, 1) and (7, 5, 1) respectively) in network structure by the artificial neural network. The correlation coefficient (r), the root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were selected as the indexes for evaluating the performance of calibration models. The results show that r, RMSECV and RSECV by the model samples for TP and TA are 0.9847, 0.460 and 0.123, and 0.9470, 0.136 and 0.224 respectively, and r, RMSEP and RSEP by the prediction samples for TP and TA are 0.9804, 0.529 and 0.017, and 0.9682, 0.111 and 0.0298 respectively. These indicated that the NIRANN models can be used to determine the contents of TP and TA in tea.