Predicting percentage of intramuscular fat using two types of real-time ultrasound equipment

In the present study, 500 steers were used to develop models for predicting the percentage of intramuscular fat (PIMF) in live beef cattle. Before sla ughter, steers were scanned across the 11th and 13th ribs using Aloka 500V (AL-600) and Classic Scanner 200 (CS-200) machines. Four to five images wer e collected per individual steer using each machine. After slaughter, a cro ss-sectional slice of the longissimus muscle from the 12th rib facing was u sed for chemical extraction to determine actual carcass percentage of intra muscular fat; (CPIMF). Texture analysis software was used by two interprete rs to select a region for determination of image parameters, which included Fourier, gradient, histogram, and co-occurrence parameters. Four predictio n models were developed separately for each of AL-500 and CS-200 based on i mages captured by the respective machines. These included models developed without transformation of CPIMF (Model I), models based on logarithmic tran sformation of CPIMF (Model II), ridge regression procedure (Model III), and principal component regression procedure (Model IV). Model R2 and root mea n square error of AL-600 Models I, II, III, and TV were 0.72, 0.84%; 0.72, 0.85%; 0.69, 0.91%; and 0.71, 0.86%; respectively. The corresponding R-2 an d root mean square error values of CS-200 Models I, II, III, and IV were 0. 68, 0.87%; 0.70, 0.85%; 0.64, 0.94%; and 0.65, 0.91%; respectively. Initial ly, AL-500 and CS-200 prediction models were validated separately on an ind ependent data set from 71 feedlot steers. The overall mean bias, standard e rror of prediction, and rank correlation coefficient across the four AL-500 models were 0.42%, 0.84%, and 0.88, respectively. For the four CS-200 mode ls, the corresponding overall mean values were 0.67%, 0.81%, and 0.91, resp ectively. In a second validation test, only Model II of AL-500 and CS-200 w as evaluated separately based on data from 24 feedlot steers. The overall m ean bias, absolute difference, and standard error of prediction of AL-500 M odel II were 0.71, 0.92, and 0.98%. For CS-200 Model II, the corresponding values were 0.59, 0.97, and 1.03%. Both AL-500 and CS-200 equipment can be used to accurately predict PIMF in live cattle. Further improvement in the accuracy of prediction equations could be achieved through increasing the d evelopment data set and the variation in PIMF of cattle used.