Origin discrimination and quality evaluation of Gastrodiae rhizoma (Orchidaceae) by high-performance liquid chromatographic fingerprint

Purpose: To develop a high-performance liquid chromatography (HPLC) fingerprint method for the quality control and origin discrimination of Gastrodiae rhizoma . Methods: Twelve batches of G. rhizoma collected from Sichuan, Guizhou and Shanxi provinces in china were used to establish the fingerprint. The chromatographic peak (gastrodin) was taken as the reference peak, and all sample separation was performed on a Agilent C18 (250 mm×4.6 mmx5 μm) column with a column temperature of 25 °C. The mobile phase was acetonitrile/0.8 % phosphate water solution (in a gradient elution mode) and the flow rate of 1 mL/min. The detection wavelength was 270 nm. The method was validated as per the guidelines of Chinese Pharmacopoeia. Results: The chromatograms of the samples showed 11 common peaks, of which no. 4 was identified as that of Gastrodin. Data for the samples were analyzed statistically using similarity analysis and hierarchical cluster analysis (HCA). The similarity index between reference chromatogram and samples’ chromatograms were all > 0.80. The similarity index of G. rhizoma from Guizhou, Shanxi and Sichuan is evident as follows: 0.854 - 0.885, 0.915 - 0.930 and 0.820 - 0.848, respectively. The samples could be divided into three clusters at a rescaled distance of 7.5: S1 - S4 as cluster 1; S5 - S8 cluster 2, and others grouped into cluster 3. Conclusion: The findings indicate that HPLC fingerprinting technology is appropriate for quality control and origin discrimination of G. rhizoma.

Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.