Prevalence of Listeria species in some foods and their rapid identification

Purpose: To investigate the occurrence of Listeria spp., (particularly L. monocytogenes ), in different foods and to compare diagnostic tools for their identification at species level. Methods: Samples of high protein foods such as raw meats and meat products and including beef products, chicken, fish and camel milk were analysed for the presence of Listeria spp. The isolates were characterised by morphological and cultural analyses, and confirmed isolates were identified by protein profiling and verified using API Listeria system. Protein profiling by SDS-PAGE was also used to identify Listeria spp. Results: Out of 40 meat samples, 14 (35 %) samples were contaminated with Listeria spp., with the highest incidence (50 %) occurring in raw beef products and raw chicken. Protein profiling by SDSPAGE was used to identify Listeria spp. The results were verified with API Listeria system. Approximately 25 % of the identified isolates were Listeria seeligeri , Listeria welshimeri , and Listeria grayi (three positive samples), while 16.66 % of the isolates were Listeria monocytogenes (two positive samples); 16.6 % of the isolates were Listeria innocua (two positive samples), while 8.3 % of the isolates were Listeria ivanovii (one positive sample). Conclusion: High protein foods contain different types of Listeria species; whole-cell protein profiles and API Listeria system can help in the identification of Listeria at the species level.

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.