Erosive potential of sugar-free hard candies dissolved in water and artificial saliva

Aim: To compare the acidity of sugar-free hard candies dissolved in water and artificial saliva. Methods: Sugar-free Flopi Florestal hard candies (grape, strawberry, cherry, orange, ginger, lemon balm, fennel) were selected and grouped in 2 groups: G-1 (candies dissolved in distilled water) and G-2 (candies dissolved in artificial saliva). Candies were triturated with a porcelain pestle, yielding two samples of 20 g. Samples were dissolved in 120 mL distilled water (G-1) and 120 mL artificial saliva (20 mM NaHCO3, 3 mM NaH2PO4.H2O and 1 mM CaCl2.2H2O) (G-2), obtaining three samples of 30 mL for each of the flavors and groups. pH was measured using potentiometer and combined glass electrode. Titratable acidity was evaluated by adding 100 μL 1M NaOH aliquots until reaching pH 5.5. For statistical analysis, analysis of variance (ANOVA) was used. Means were compared by the Tukey test at 5% significance level (p<0.05) Results: All flavors of G-1 showed pH values below 5.5. Comparison of groups in the same flavor showed a significant increase in pH in flavors of G-2. Comparison of the titratable acidity between G-1 and G-2, showed that fruit flavors were significantly different from each other, with reduced acidity in G-2. Conclusions: All evaluated candies are acid, and dilution in artificial saliva raised their pH and lowered their titratable acidity, reducing their erosive potential.

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.