Estimation of pork quality traits using exsanguination blood and postmortem muscle metabolites

Genome-wide association studies to identify quantitative trait loci and positional candidate genes affecting meat quality-related traits in pigs

Meat quality comprises a set of key traits such as pH, meat color, water-holding capacity, tenderness and marbling. These traits are complex because they are affected by multiple genetic and environmental factors. The aim of this study was to investigate the molecular genetic basis underlying nine meat quality-related traits in a Yorkshire pig population using a genome-wide association study (GWAS) and subsequent biological pathway analysis. In total, 45,926 single nucleotide polymorphism (SNP) markers from 543 pigs were selected for the GWAS after quality control. Data were analyzed using a genome-wide efficient mixed model association (GEMMA) method. This linear mixed model-based approach identified two quantitative trait loci (QTLs) for meat color (b*) on chromosome 2 (SSC2) and one QTL for shear force on chromosome 8 (SSC8). These QTLs acted additively on the two phenotypes and explained 3.92%-4.57% of the phenotypic variance of the traits of interest. The genes encoding HAUS8 on SSC2 and an lncRNA on SSC8 were identified as positional candidate genes for these QTLs. The results of the biological pathway analysis revealed that positional candidate genes for meat color (b*) were enriched in pathways related to muscle development, muscle growth, intramuscular adipocyte differentiation, and lipid accumulation in muscle, whereas positional candidate genes for shear force were overrepresented in pathways related to cell growth, cell differentiation, and fatty acids synthesis. Further verification of these identified SNPs and genes in other independent populations could provide valuable information for understanding the variations in pork quality-related traits.

Hierarchical clustering as a tool to develop a classification scheme for rabbit meat quality

<p>This study aimed to characterise the quality of meat from commercially-raised rabbits. Animals came from five different producers and were  laughtered in three different plants under provincial or federal inspection jurisdiction. Animal behaviour evaluated by scan sampling prior to feed withdrawal (FW) and transport, as well as blood lactate concentration at exsanguination, did not raise concerns with respect to stress. Stomach pH was higher (<em>P</em>=0.047) when the FW time was short (≤13.5 h), at a mean value of 2.23. All pH values measured 1 h post-mortem from the Biceps femoris (BF) and almost all (97.6%) from the Longissimus lumborum (LL) were higher than 6. Values for ultimate pH measured 24 h postmortem(pH_u) ranged from 5.80 to 6.83 and from 5.70 to 6.70 for BF and LL muscles, respectively. The maximum meat drip loss recorded was 2.6%, while cooking loss reached 30%. Meat lightness (L*) and colour intensity (C*) for the long FW times (≥23 h) were no different from those with short and intermediate (15.5 to 17.3 h) FW times. However, these colour parameters were higher for the short FW time class compared to the intermediate FW time class (<em>P</em>&lt;0.02). A hierarchical cluster analysis based on pH_u, cooking loss and lightness (L*) from 200 rabbit loins was performed. Of the four clusters created, clusters 1 and 2 had the best and second-best meat quality, respectively. Clusters 3 and 4 had the lowest meat quality and presented DFD-like (dark, firm and dry) characteristics. Meat did not exhibit PSE-like (pale, soft, exudative) characteristics, even for the slaughter lot with the minimum mean pH_u. Of the eight slaughter lots evaluated, more than 50% of the meat from three of them fell into clusters 3 and 4; all three were in the intermediate FW time class. Overall, the quality of rabbit meat analysed was acceptable for commercial use, but rather variable. This suggests that there are factors within the value chain that are not yet fully controlled and require further investigation.</p>

Biosensors in Evaluation of Quality of Meat and Meat Products – A Review

Biosensors can find application in meat and meat products testing for safety, including microbial and other contaminants, and quality, including meat freshness, beef tenderness and pork quality defects. The available biosensors enable the evaluation of freshness, the classification of tenderness of meat products, the evaluation of the glycolysis extent and the presence of the microbial and other contaminants. Since biosensors depend on receptor types, the expansion of knowledge on metabolic transformations occurring in meat contributes to the development of new potential markers and indicators. Examples include assays for glucose, lactates, hypoxanthines, calpastastins, microbial and other contaminants in meat products, augmenting conventional methods. At the same time, biosensors rely on transducers for detection, requiring achievement in many fields including nanotechnology and optics, among others. Biosensors have potential to become a fundamental tool for monitoring and controlling safety and quality of meat products in the future. Hence the aim of the present paper is to present the current state of knowledge on the application of biosensors in meat.

Development of class model based on blood biochemical parameters as a diagnostic tool of PSE meat

A fast, sensitive and effective method based on the blood biochemical parameters for the detection of PSE meat was developed in this study. A total of 200 pigs were slaughtered in the same slaughterhouse. Meat quality was evaluated by measuring pH, electrical conductivity and color at 45min, 2h and 24h after slaughtering in M. longissimus thoracis et lumborum (LD). Blood biochemical parameters were determined in blood samples collected during carcass bleeding. Principal component analysis (PCA) biplot showed that high levels of exsanguination Creatine Kinase, Lactate Dehydrogenase, Aspertate aminotransferase, blood glucose and lactate were associated with the PSE meat, and the five biochemical parameters were found to be good indicators of PSE meat Discriminant function analysis (DFA) was able to clearly identify PSE meat using the five biochemical parameters as input data, and the class model is an effective diagnostic tool in pigs which can be used to detect the PSE meat and reduce economic loss for the company.