Prediction of quantitative swine carcass traits by the real-time ultrasonicgraphy technology

[摘要] - Forty-eight female pigs were used to determine the commercial cut yields after slaughter of the animals at 50, 60, 70, 80, 90, 100, 110 and 120 kg and dissection of the carcasses. Life ultrasonic measures were taken to measure the longissimus muscle area and for backfat thickness. The predictors used to estimate the lean meat yield were backfat thickness and longissimus muscle area. Besides the measures made in the different cuts, as ham cut weight, ham muscle weight, palette muscle weight, and carré muscle weight and pantry muscle weight. Additionally, the data of the total carcass muscle weight, total carcass fat weight, total carcass bone weight and total carcass skin weight, were also used. Prediction equations were developed using simple and multiple linear regressions using the logistic model from the dissection data and from the live ultrasonic measure data. Prediction equations were developed through out simple and multiple linear regressions using a logistic model from the dissection and live measures made by real-time ultrasound technology data. The prediction equation to determine the amounts the main components of the meat pork cuts presented a great adjustment, with R2 superiors to 0.84 for muscles, with residual standard deviation (RSD) 0.62 to 0.94. The real-time ultrasonograph technology presented high correlation with the measures taking in the pork carcasses and could be used to estimate the amount of lean meat in the pork carcasses and also to estimate the commercial pork cut yields with great precision. It was concluded that the real-time ultrasonograph technology is accurate to predict the main pork carcass traits measured in the live animal.

[学科分类] 兽医学

[关键词] backfat thickness;commercial cuts;loin eye area;real-time;ultrasonicgraphy   IntroduçãoAumentar a quantidade e melhorar a qualidade da carne na carcaça de suínos tem sido o objetivo não somente da indústria;como também do produtor de suínos;pois melhora a rentabilidade e diminui os custos de produção