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Prediction of crude fat content of longissimus muscle of beef using the ratio of fat area calculated from computer image analysis: Comparison of regression equations for prediction using different input devices at different stations

机译:使用计算机图像分析计算出的脂肪面积比率预测牛肉的长肌粗脂肪含量:在不同站点使用不同输入设备进行预测的回归方程比较

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摘要

Crude fat content of longissimus (ribeye) muscle of beef cattle was predicted from a ratio of fat area (RFA) to area of ribeye muscle calculated from computer image analysis (CIA). Cross sections of 64 ribeyes taken from the 6-7(th) rib from cattle at experiment station A and cross sections of 94 ribeyes taken from the 6-7(th) rib from cattle at Experiment Station B were used in this study. Slices (1 to 1.5 cm in thickness) of just the Longissimus dorsi were homogenized and sampled for chemical estimation of crude fat content using petroleum ether. Crude fat content as determined from chemical analysis was used as the true estimate of fat content. A CCD (charge-coupled device) camera was used as the input device at Experiment Station A, and a single-lens reflex camera was used at Experiment Station B to photograph ribeyes for CIA. The contour comparison method, which assigns a threshold value for each marbling particle, was used to obtain accurate binarization in this study. Minimum and maximum of chemical measurements of crude fat were 2.1 and 39.8%, and for CIA calculation of the RFA were 6.1 and 56.8%, respectively. This range covered almost the complete range of the beef marbling standard used in carcass grading in Japan. The equation for the regression of the crude fat content (Y) on RFA (X) calculated from CIA for all of the data was Y = .793X - 3.04 with r(2) = .96. Regression equations for prediction of crude fat percentage from RFA taking into consideration the effect of experiment station were Y = .741X - 2.22 with r(2) = .91 for Experiment Station A, and Y = .782X - 2.54 with r(2) = .91 for Experiment Station B. Analysis of covariance showed that the effects of experiment stations on intercepts and slopes were not significant (P > .10). The ranges of differences between actual and predicted crude fat content from the prediction equation that was calculated without consideration of the effect of station were - 6.4 to 4.0%. CIA of cross sections of the ribeye muscle seems to have potential for prediction of crude fat content.
机译:根据计算机图像分析(CIA)计算得出的脂肪面积(RFA)与肋眼肌面积之比,可以预测肉牛的最长肌(肋眼)肌肉的粗脂肪含量。在本研究中,使用了来自实验站A的牛的6-7(th)肋骨的64个肋眼的横截面和来自实验站B的牛的6-7(th)肋骨的94肋眼的横截面。均质化Longissimus dorsi的切片(厚度为1至1.5 cm)并取样,以使用石油醚进行化学估算粗脂肪含量。通过化学分析确定的粗脂肪含量被用作脂肪含量的真实估计。实验站A使用CCD(电荷耦合器件)相机作为输入设备,实验站B使用单镜头反光相机为CIA拍摄肋眼。在这项研究中,使用轮廓比较法(为每个大理石花纹颗粒指定阈值)来获得准确的二值化。粗脂肪的化学计量的最小值和最大值分别为2.1和39.8%,对于CIA而言,RFA的分别为6.1和56.8%。该范围几乎涵盖了日本in体分级所使用的牛肉大理石花纹标准的全部范围。对于所有数据,从CIA得出的RFA(X)上的粗脂肪含量(Y)的回归方程为Y = .793X-3.04,r(2)= .96。考虑到实验站的影响,从RFA预测粗脂肪百分比的回归方程为:对于实验站A,r = 2.741X-2.22,其中r(2)= .91;对于r(2),Y = .782X-2.54 =实验站B的= 0.91。协方差分析表明,实验站对截距和坡度的影响不显着(P> .10)。根据预测方程式计算的实际和预测粗脂肪含量之间的差异范围是-6.4%至4.0%。肋眼肌横截面的CIA似乎具有预测粗脂肪含量的潜力。

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