The Influence Of Slaughter Stress On Blood Biochemical Index And Carcass Quality In Pigs

China was the biggest country of pork production and consumption, but now suffered more and more PSE meat problems which had plagued the developed countries for years. Domestic pork industry was mainly dominated by hot meat. Carcass could be split to retail in the farmers market after the first and second wholesale market. Due to the lack of pre-screening method for detecting PSE meat rapidly, it was generally found in retail part on the next day. So the value and utilization of meat would decline severely and it would cause serious economic loss. One stress-related effect on pork was called pale, soft, exudative (PSE) meat as a consequence of numerous factors,such as genetics, transport time, loading density, stunning method et al. Stunning method (electrical stunning) seemed to be the most important factor to produce PSE meat under the existing breeds, breeding and transportation conditions after conducting a survey about slaughterhouse in Zhejiang province. Then many slaughtering enterprises began to hang living pigs and bleed without electrical stunning. This paper aimed to study the effect of stunning method on blood biochemical index and carcass quality in order to find the bio-markers which could indicate the PSE meat; then establish the prediction model of serum samples which took different stunning method by using BP neural network.Test one:In this experiment, we chose800crossbred (Duroc×Landrace×Yorkshire) pigs with average body weight of100kg. And they all qualified healthy under the ante-mortem inspection. Summer and autumn had400respectively, and then they were divided into two groups:electrical stunning and hang without stunning (each group200) Serum samples were collected from each pig and measured biochemical parameters. Meanwhile,40longissimus muscle were selected randomly from each group and measured the carcass quality. The results showed that:electrical group exhibited higher TP, GLU, BUN, CREA, TQ Na+when compared to hanging group, whereas K+, apoA-I were lower than the hanging group; the concentration of GLU, BUN, CREA, TCH, TQ Na+in summer group were significantly higher than the autumn group, and AST, AKP, CK, K+, apoA-I decreased in summer group; electrical stunning could cause greater stress; in summer, the incidence of PSE meat was38%in electrical group and16%in hanging group; and in autumn, the incidence of PSE meat was16%in electrical group and3%in hanging group. It was conducted that electrical stunning was easier to produce stress response, and GLU, BUN, CREA, TG, Na+, K+, apoA-I could be used as indicator to characterize the degree of stress.Test two:In this experiment, we chose300crossbred (Duroc×Landrace×Yorkshire) pigs with average body weight of100kg And they all qualified healthy under the ante-mortem inspection. And they were divided into two groups:electrical stunning in summer and hang without stunning in autumn (each group150). Serum samples were collected from each pig and measured biochemical parameters. Meanwhile, longissimus muscle were measured to assess the meat quality. A total of14biochemical parameters which could reflect the degree of stress were extracted by principal component analysis (PCA) to eliminate liner relevance, then7principal components without correlation had been obtained and could reflect about85%of the original information effectively. Almost seven blood biochemical parameters were extracted after rotating the loading matrix (including GLU, BUN, CREA, TG, Na+K+, apoA-I). Then BP neural network with three layers was used to train and forecast the data:280samples were selected from the raw data randomly as training data, the remaining20samples as testing data. The results showed that:compared to the BP neural network without principal component analysis, PCA-BP neural network could classify accurately and the correct rate was up to95%. The PCA-BP neural network had almost the same predictive ability as the traditional BP neural network, and could simplify the network structure. It was conducted that the methods integrating PCA with BP neural network could precisely classify the serum sample and provide a new approach to evaluate the quality meat.