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Target-guided isolation and purification of antioxidants from Elsholtzia densa benth. var. densa by DPPH antioxidant assay and dual-mode HSCCC

Zhou W. N., Ouyang J., Wang Z. H., Wang X. Y., Suo Y. R., Zhang Z., Wang H. L.

Acta Chromatographica

2018

Vol. 30, no. 3

180--185

Elsholtzia densa Benth. var. densa (Lamiaceae) is a famous medicinal herb which has been widely used for treatment of colds, headaches, pharyngitis, fever, diarrhea, digestion disorder, rheumatic arthritis, nephritises, and nyctalopia in China. In this study, fraction of the ethyl alcohol extract of E. densa (aerial part) by different polarity solvents indicated that the ethyl acetate soluble fraction exhibited a potent 1,1-diphenyl-2-picryhydrazyl (DPPH) radical scavenging activity with the IC50 value of 148.2 μg/mL. Under the target guidance of DPPH experiment, isoquercitrin, trachelogenin, ethyl caffeate, and arctigenin were separated with purities 95.98%, 92.98%, 96.07%, and 88.83%, respectively, by a dual-mode high-speed counter-current chromatography (HSCCC) method using n-hexane–ethyl acetate–methanol–water (4.5:5:3:4, v/v/v/v) as the solvent system. In order to evaluate the scientific basis, antioxidant activity of four isolated compounds was assessed by the radical scavenging effect on DPPH radical; isoquercitrin and ethyl caffeate showed stronger antioxidant activities with IC50 values of 9.4 μg/mL and 9.2 μg/mL, respectively, while trachelogenin and arctigenin showed weak antioxidant activities with IC50 values of >500 μg/mL and 72.8 μg/mL, respectively. Results of the present study indicated that the combinative method using DPPH antioxidant assay and dual-mode HSCCC could be widely applied for rapid screening and isolating of antioxidants from complex traditional Chinese medicine extract.

A Color- and Texture-based Image Segmentation Algorithm

Wang X. Y., Sun Y. F.

Machine Graphics and Vision

2010

Vol. 19, No. 1

3-18

Image segmentation is a classic inverse problem which consists in obtaining a compact, region-based description of the image scene by decomposing it into meaningful or spatially coherent regions sharing similar attributes. Because a color image can provide more perceptual information, color image segmentation is being paid more and more attention. In this paper, we propose a new approach to color image segmentation that is based on low-level features of color and texture. The approach is aimed at segmentation of natural scenes where the color and texture of each segment do not typically exhibit uniform statistical characteristics. Firstly, local color composition is described in terms of spatially adaptive dominant colors by using the Gibbs random field, and the color image is segmented into regions according to the local color composition. Secondly, the texture characteristics of the grayscale component are described by utilizing the Steerable filter, and the grayscale component of color image is cut into flat regions and non-flat regions. Thirdly, the local color composition and texture characteristics are combined to obtain an overall crude segmentation. Finally, an elaborate border refinement procedure is used to obtain accurate and precise border localization by appropriately combining color-texture features with the Normalized Cuts. The experimental results demonstrate that the color image segmentation results of the proposed approach exhibit favorable consistency in terms of human perception.