Department of Test Engineering, IGT, Reno, NV 89521, USA.
Meat Sci. 2010 Jun;85(2):240-4. doi: 10.1016/j.meatsci.2010.01.005. Epub 2010 Jan 13.
Video images of ham cross-sections were recorded from 71 pork carcasses (ranging in weight from 72 to 119kg). Three sets of prediction equations were developed to estimate pork carcass lean and fat composition from video image analysis (VIA) of ham cross-sectional area measurements, 10th rib back fat depth (TENFAT) and hot carcass weight (HCKg). Carcass data of dissected lean and fat in the four primal cuts (ham, loin, Boston button and picnic shoulder) were used as dependent variables in establishing regression equations. The first set of equations combined VIA ham measurements and total ham weight (HTKg). Regression models containing the single variable HTKg times ham percentage lean area (Vol. 1) or HTKg times ham percentage fat area (Vol. 2) accounted for 88% and 68% of the variation in total carcass lean weight (CLKg) and total carcass fat weight (CFKg) from the right side of each carcass, respectively. The second set of equations combined VIA ham measurements and TENFAT (cm). Multiple regression models involving TENFAT, Vol. 1, and Vol. 2 accounted for 91% and 90% of the variation in CLKg and CFKg. The third set of equations used VIA ham measurements, TENFAT and HCKg. Carcass lean weight was best predicted by HCKg, TENFAT, and ham lean area (HLA) (R(2)=.92). Carcass fat weight was best predicted by HCKg, TENFAT, and Vol. 2 (R(2)=.91). Overall correlations showed a high association between Vol. 1 and CLKg (r=.94, P<.0001) and Vol. 2 and CFKg (r=.83, P<.0001). Ham lean area was related to CLKg (r=.74, P<.0001) and ham fat area to CFKg (r=.81, P<.0001). The results of this study indicated video image analysis of ham cross-section slices combined with backfat depth at the 10th rib can be used for accurate estimation of total carcass lean or fat composition.
从 71 头猪肉胴体(体重范围为 72 至 119 公斤)中记录了火腿横截面的视频图像。开发了三套预测方程,以通过视频图像分析(VIA)火腿横截面面积测量、第 10 肋骨背膘厚度(TENFAT)和热胴体重量(HCKg)来估计猪肉胴体的瘦肉和脂肪组成。在建立回归方程时,将四个主要切块(火腿、里脊、波士顿按钮和野餐肩)中分割的瘦肉和脂肪的胴体数据用作因变量。第一组方程结合了 VIA 火腿测量值和总火腿重量(HTKg)。包含单个变量 HKTG 乘以火腿瘦肉面积百分比(Vol.1)或 HKTG 乘以火腿脂肪面积百分比(Vol.2)的回归模型分别解释了来自每头胴体右侧的总胴体瘦肉重量(CLKg)和总胴体脂肪重量(CFKg)变化的 88%和 68%。第二组方程结合了 VIA 火腿测量值和 TENFAT(cm)。涉及 TENFAT、Vol.1 和 Vol.2 的多元回归模型解释了 CLKg 和 CFKg 变化的 91%和 90%。第三组方程使用 VIA 火腿测量值、TENFAT 和 HCKg。胴体瘦肉重量最好由 HCKg、TENFAT 和火腿瘦肉面积(HLA)(R²=0.92)预测。胴体脂肪重量最好由 HCKg、TENFAT 和 Vol.2(R²=0.91)预测。总体相关性表明 Vol.1 和 CLKg(r=.94,P<.0001)以及 Vol.2 和 CFKg(r=.83,P<.0001)之间存在高度关联。火腿瘦肉面积与 CLKg(r=.74,P<.0001)相关,火腿脂肪面积与 CFKg(r=.81,P<.0001)相关。本研究结果表明,结合第 10 肋骨背膘厚度的火腿横截面切片的视频图像分析可用于准确估计总胴体瘦肉或脂肪组成。