Determining the presence of chicken and turkey meat in selected meat products using realtime PCR method

The one of the most convenient method for the identification of animal species in raw and processed meat products is the examination of DNA sequences. Real-Time PCR are particularly suitable because even small fragments of DNA formed during heat processing of the meat can be amplified and identified. TaqMan Real-Time PCR is a rapid, convenient and sensitive assay for meat identification. For chicken and turkey meat identification we were using species-specific primers and TaqMan probe designed on the mitochondrial cytochrome b. The intensity of the fluorescence signal has risen at a variety of different samples. We analysed sixteen the samples of turkey meat products and we found the incidence of chicken at nine samples in the range of the detection range of the reaction0.1 to 100%.  Sample 8 fluorescence intensity exceeded the detection threshold in the 22.11 cycle (Cp = 22.11); Sample 6, (Cp = 23.19); Sample 1 in 27.08 cycle (Cp = 27.08); Sample 7 in 31,7 cycle (Cp = 31.7) and sample 5 in 32.32 cycle (Cp = 32.32). All Cp values for these samples fluorescence intensity exceeded the detection threshold in earlier cycles as sample the 100% turkey DNA. It follows that in the samples no. 8, 6, 1, 5, and 7 is in the range of chicken DNA detection range of the reaction, from 0.1 to 100%. Sample 11 in the cycle 27,08 (Cp = 27.08); Sample 10 in the cycle 27.8 (Cp = 27.8); sample 16 in 28.03 cycle (Cp = 28.03) and sample 13 in the cycle of 29.18 (Cp = 29.18). In recognition of the results of the monitoring of the content of chicken meat in meat products it is appropriate to further verification and testing detection kits used to work for possible use in practice since it has been found to be sufficient sensitivity and specificity to 30 cycle reaction

Determination of the species specificity of the primers for the detection of chicken and turkey meat by realtime PCR method

The aim of this work was to use TaqMan Real-Time PCR for quantitative authentication of chicken and turkey meat. To meet this purpose, a specific pair of primers and TaqMan probe was used. The test was aimed at identifying the reaction cycle of turkey and chicken meat using by two sets of primers. With first set of primer designed for chicken we obtained the following results: Cp = 16.18 for 100% chicken DNA Cp = 29, 18 100% turkey DNA It was also amplified DNA of pig that exceeded the detection threshold fluorescence intensities in the 31.07 cycle (Cp = 31.07). Using primers designed for turkey we obtained the following results Cp = 31.16 for 100% CHDNA, Cp =16.18 100% TDNA. It was also amplified the 100% DNA of rabbit in 31.63 cycle (Cp = 31.63) and deer in cycle 32 (Cp = 32). The DNA of all other animal species was amplificated after more than 35 cycles (Cp &gt;35). It follows that the second detection primer pair is specific enough to unrelated species of animals by 30 cycles of the reaction. Species authentication based on DNA analysis from this perspective overcomes all the shortcomings of proteins. At present, DNA analysis use different types of PCR. Is the most progressive Real-time PCR, which is suitable for the specific use of detection (primers and TaqMan probe). The TaqMan Real-time PCR is within the sensitivity and specificity, clearly one of the best methods for identifying the species of chicken and turkey meat. The specificity of this method, however, depends primarily on the specificity of the primers and TaqMan probe. The 30 cycle reaction was chosen by us as the threshold for specificity using primers for authentication chicken and turkey meat.<br /

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images.

Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images

Comparative analysis of wavelet transform filtering systems for noise reduction in ultrasound images

Wavelet transform (WT) is a commonly used method for noise suppression and feature extraction from biomedical images. The selection of WT system settings significantly affects the efficiency of denoising procedure. This comparative study analyzed the efficacy of the proposed WT system on real 292 ultrasound images from several areas of interest. The study investigates the performance of the system for different scaling functions of two basic wavelet bases, Daubechies and Symlets, and their efficiency on images artificially corrupted by three kinds of noise. To evaluate our extensive analysis, we used objective metrics, namely structural similarity index (SSIM), correlation coefficient, mean squared error (MSE), peak signal-to-noise ratio (PSNR) and universal image quality index (Q-index). Moreover, this study includes clinical insights on selected filtration outcomes provided by clinical experts. The results show that the efficiency of the filtration strongly depends on the specific wavelet system setting, type of ultrasound data, and the noise present. The findings presented may provide a useful guideline for researchers, software developers, and clinical professionals to obtain high quality images.Web of Science177art. no. e027074