Study on Predict the Carcass and Beef Quality of Kerchin Cattle
|Course||Agricultural Products Processing and Storage|
|Keywords||Horqin cattle Carcass quality Beef Quality Near Infrared Spectroscopy Prediction model|
In this paper, Horqin cattle study, correlation regression analysis of the the intuitive measured traits of carcass traits and post-mortem to establish the practical prediction of carcass meat yield and meat production rate equation, in accordance with the different meat production performance beef carcass grading to establish a scientific and reasonable good quality and inexpensive acquisition system. Investigated near-infrared spectroscopy applications in the beef quality assessment aspects, mainly near-infrared quantitative tenderness of the Grading. Carcass quality assessment, select 60 the adult Kerqin cattle, slaughter, cooling, split, cow body were measured liveweight (LW), hot carcass weight (HCW), loin eye area (REA), backfat thickness ( FT) and carcass traits indicators using SPSS 16.0 software for data analysis and multiple linear regression, and ultimately get the three factors jointly predict cattle carcass weight (HCW), cattle body live weight (LW) and loin eye area (REA) meat production, the amount of linear equations Y1 = -7.357 1.122 × HCW a 0.155 × LW 0.077 × REA (R2 = 0.986, RMSE of = 2.842) and prediction of lean percentage of linear equations Y2 = 42.971 0.182 × HCW-0.098 × LW 0.021 × REA (R2 = 0.930, RMSE = 0.516), results showed that the two equations can be used in the Horqin bovine meat production and the prediction of the lean percentage in the actual production. Rapid assessment of beef quality using near infrared spectroscopy, the quantitative detection of the main component of beef protein, fat and moisture content. The effects of different pretreatment methods, the best modeling band and the main factor dimension such as partial least squares method (PLS) method were established predictive models of protein, fat and moisture content, the resulting prediction model coefficients are large at 0.90, the prediction accuracy and stability, achieve better results. Using near infrared spectroscopy to predict beef tenderness near infrared spectroscopy model level predictive analysis of shear force value and tenderness of beef samples using partial least squares (PLS). Smoothing and second derivative pretreatment, 12000cm-1 ~ 4000cm-1 wave number range of the entire region, the establishment of a near-infrared quantitative model of beef shear force value of the spectrum. When the collected spectra beef samples for meat state model coefficient of determination (R2) and the school ended the root mean square error (RMSEC) was 0.512 and 1.427, respectively, when the collected spectra beef samples for minced meat, the coefficient of determination (R2) and forecast root mean square error (RMSEP) is 0.669 and 0.963, respectively, indicating that the acquisition in the meat emulsion state the sample spectra establishment of near infrared prediction the Beef Tenderness model effect better. As minced meat, beef samples collected spectra the forecast model grade of beef tenderness prediction accuracy rate of 88.8%. When the collected spectra beef samples for meat, the forecast model grade of beef tenderness prediction accuracy rate of 83.3%. Grade of beef tenderness prediction assessment is feasible using near-infrared technology.