Average backfat thickness measurements (liveweight of 92.5 kg) were made on 276 pigs using the Krautkramer USK7 ultrasonic machine. Immediately preceding and 1 h after slaughter, real-time ultrasonic images were made between the 3rd and 4th last ribs with the Tokyo Keiki LS-1000 (n = 149) and/or CS-3000 (n = 240) machines. Image analysis software was used to measure fat thickness (FT), muscle depth (MD) and area (MA) as well as scoring the number of objects, object area and percentage object area of the loin to be used for predicting meat quality. Carcasses were also graded by the Hennessy Grading Probe (HGP). Prediction equations for lean in the primal cuts based on USK7 and LS-1000 animal fat measurements had R(2)-values (residual standard deviations, RSD) of 0.62 (27.0) and 0.66 (25.7). Adding MD or MA to LS-1000 FT measurements increased the R(2)-values to 0.68 and 0.66. Prediction equations using animal fat measurements made by the USK7 and CS-3000 had R(2)-values (RSD) of 0.66 (26.5) and 0.76 (22.4). Adding MD or MA to CS-3000 FT measurements made no further improvement in the R(2)-values. Estimation of commercial lean yield from carcass FT and MD measurements made by the HGP and LS-1000 had R(2)-values (RSD) of 0.58 (1.72) and 0.65 (1.56). Adding MA to LS-1000 measurements made no further improvement in the R(2)-values. Prediction equations based on carcass FT and MD measurements made by the HGP and CS-3000 had R(2)-values (RSD) of 0.68 (1.65) and 0.72 (1.54). Adding MA to CS-3000 measurements made no further improvement in the prediction equations. It was concluded that RTU has most value for predicting carcass lean content and further improvements in precision will come from more accurate FT measurements from RTU images made by image analysis software. Correlation of the number of objects, object area and of percent object area of image from RTU images with intramuscular fat or marbling score made on the live pig or carcass were low, and presently do not appear suitable for predicting intramuscular fat.
机构:
Ctr Invest Montanha, Escola Super Agr Braganca, P-5301855 Braganca, PortugalCtr Invest Montanha, Escola Super Agr Braganca, P-5301855 Braganca, Portugal
Teixeira, A.
Joy, M.
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CITA, Unidad Tecnol Prod Anim, Zaragoza, SpainCtr Invest Montanha, Escola Super Agr Braganca, P-5301855 Braganca, Portugal
Joy, M.
Delfa, R.
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CITA, Unidad Tecnol Prod Anim, Zaragoza, SpainCtr Invest Montanha, Escola Super Agr Braganca, P-5301855 Braganca, Portugal
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Embrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, BrazilEmbrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, Brazil
Dalla Costa, Osmar Antonio
Feddern, Vivian
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Embrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, BrazilEmbrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, Brazil
Feddern, Vivian
Athayde, Natalia Bortoleto
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Embrapa Acre, Rod BR 364,Km 14, BR-69900970 Rio Branco, AC, BrazilEmbrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, Brazil
Athayde, Natalia Bortoleto
Manzke, Naiana Einhardt
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Kerry Do Brasil Ltda, Av Mercedes Benz 460, BR-13054750 Campinas, SP, BrazilEmbrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, Brazil
Manzke, Naiana Einhardt
Roca, Roberto de Oliveira
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Sao Paulo State Univ, FCA, Econ Sociol & Technol Dept, CP 237, BR-18603970 Botucatu, SP, BrazilEmbrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, Brazil
Roca, Roberto de Oliveira
Lopes, Leticia dos Santos
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Embrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, BrazilEmbrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, Brazil
Lopes, Leticia dos Santos
Mello Monteiro de Lima, Gustavo Julio
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Embrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, BrazilEmbrapa Swine & Poultry, Rod BR 153,Km 110, BR-89715899 Concordia, SC, Brazil