BackgroundNatural products have being used as potential inhibitors against carbohydrate-hydrolyzing enzymes to treat diabetes mellitus. Chinese dark tea has various interesting bioactivities. In this study, the active compounds from Qingzhuan dark tea were separated and their anti-diabetic activity was examined using an in vitro enzymatic model.MethodsThe chloroform, ethyl acetate, n-butanol, sediment and residual aqua fractions of a Chinese dark tea (Qingzhuan tea) were prepared by successively isolating the water extract with different solvents and their in vitro inhibitory activities against α-glucosidase were assessed. The fraction with the highest inhibitory activity was further characterized to obtain the main active components of Qingzhuan tea.ResultsThe ethyl acetate fraction had the greatest inhibitory effect on α-glucosidase, followed by n-butanol, sediment and residual aqua fractions (with the IC50 values of 0.26 mg/mL, 2.94 mg/mL, 3.02 mg/mL, and 5.24 mg/mL, respectively), mainly due to the high content of polyphenols. Among the eight subfractions (QEF1-8) isolated from the ethyl acetate fraction, QEF8 fraction showed the highest α-glucosidase inhibitory potential in a competitive inhibitory manner (the Ki value of 77.10 μg/mL). HPLC-MS analysis revealed that (−)-epigallocatechin gallate (EGCG) and (−)-epicatechin gallate (ECG) were the predominant active components in QEF8.ConclusionThese results indicated that Qingzhuan tea extracts exerted potent inhibitory effects against α-glucosidase, EGCG and ECG were likely responsible for the inhibitory activity in Qingzhuan tea. Qingzhuan tea may be recommended as an oral antidiabetic diet.

Withering is the first step in the processing of congou black tea. With respect to the deficiency of traditional water content detection methods, a machine vision based NDT (Non Destructive Testing) method was established to detect the moisture content of withered leaves. First, according to the time sequences using computer visual system collected visible light images of tea leaf surfaces, and color and texture characteristics are extracted through the spatial changes of colors. Then quantitative prediction models for moisture content detection of withered tea leaves was established through linear PLS (Partial Least Squares) and non-linear SVM (Support Vector Machine). The results showed correlation coefficients higher than 0.8 between the water contents and green component mean value (G), lightness component mean value (L*) and uniformity (U), which means that the extracted characteristics have great potential to predict the water contents. The performance parameters as correlation coefficient of prediction set (Rp), root-mean-square error of prediction (RMSEP), and relative standard deviation (RPD) of the SVM prediction model are 0.9314, 0.0411 and 1.8004, respectively. The non-linear modeling method can better describe the quantitative analytical relations between the image and water content. With superior generalization and robustness, the method would provide a new train of thought and theoretical basis for the online water content monitoring technology of automated production of black tea.

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