Study On Predict The Carcass And Beef Quality Of Kerchin Cattle

The aim of this study was to assess the relationship of live and carcass character of Kerchin cattle. The purpose is to find out the rapidly calculate method for the weight and percent of retail cuts on the cattle slaughtering line, to grade Kerchin cattle according to their carcass quality and to realize the policy of selling good quality cattle at a high price. At the same time in order to assess near infrared reflectance spectroscopy as a tool for rapidly dertermining of the chemical composition and tenderness of the Kerchin beef.In order to evaluate the carcass quality,60 Kerchin cattle were slaughtered, chilled and cut. Live and carcass character were measured as prediction factors of weight and percent of retail cuts. The predictors were used to construct by correlation analysis and multiple linear regression based on the SPSS 16.0. The equations for prediction of weight and percent of retail cuts were Y1=-7.357+1.122×HCW-0.155×LW+0.077×REA(R2=0.986, RMSE=2.842) and Y2=42.971+0.182×HCW-0.098×LW+0.021×REA (R2=0.930, RMSE=0.516), respectively. The equations obtained could be used for prediction of weight and percent of retail cuts of Kerchin cattle.Another thesis focuses on the quantitative calibration of protein, fat and water in beef. It explores the different pretreatment, the optimal modeling wave bands and so on. It develops the protein,fat and water calibration with PLS method. Good results are obtained which correlation coefficients are above 0.90, so the result can be accepted. The prediction and the stability are both settle for the demand of the production.Beef tenderness evaluation was studied using near infrared spectroscopy with the spectra of beef samples being collected between 4 000 and 10 000cm-1. The use of second derivative and smooth gave best predictions together with PLS.The coefficient of determination and root mean square error of calibration were 0.512 and 1.427 with meat silice, and the coefficient of determination and root mean square error of cross-validation were 0.669 and 0.963 with minced meat which gave best prediction for beef tenderness. Furthermore, the sample were classified into tender and tough classes, with a correct classification of 88.8% of minced meat, and 83.3% with meat silice. The result indicates that NIR spectroscopy is capable of predicting tenderness grade of beef.