利用超声波图像活体预测北京黑猪肌内脂肪含量

摘要

本研究旨在利用超声波图像来活体预测北京黑猪背最长肌肌内脂肪含量.试验选用382头北京黑猪开展活体测定,性状包括体质量、10～11肋间眼肌面积、背膘厚和眼肌深度,并且采集猪左侧9～13肋之间距离背中线5 cm处纵向超声波图像2张,在每张图像10～11肋间选定1个大小为80×80像素的区域作为研究对象,利用图像分析软件(matlab)分别获取了灰度梯度、灰度共生矩阵、小波变换3类图像参数.屠宰后立刻取胴体左侧10～11肋间背最长肌肉样,利用石油醚抽提法测定肌内脂肪含量(IMF).以实际测定肌内脂肪含量(IMF)为因变量,体质量、背膘厚、眼肌面积、眼肌深度和图像参数为自变量,通过逐步回归分析法建立预测肌内脂肪含量(PIMF)的模型.重新选择112头北京黑猪利用实测肌内脂肪含量与预测肌内脂肪含量的相关分析进行模型验证.回归分析结果表明,有9个自变量达到显著水平(P＜0.05),包括背膘厚、眼肌面积和7个图像参数.预测模型的决定次数(R-Square)和均方误差根(Root MSE)分别为0.305 8和0.006 5.相关性分析结果得到皮尔逊积矩相关系数(Pearson Correlation Coefficients)和斯皮尔曼相关系数(Spearman Correlation Coefficients)分别为0.553 4和0.627 2(P＜0.000 1).结果表明,利用超声波图像活体预测猪肌内脂肪含量是一种可行的方法,可为北京黑猪的育种工作提供一个技术基础.%This research was conducted to predict the intramuscular fat percentage in longissimus muscle of live Beijing Black pig using real-time ultrasound image. The loin muscle area across the 10th to 11th rib, body weight, backfat thickness, loin muscle deepness and two longitudinal real-time ultrasound images were collected across the 9th to 13th rib and 5 cm off-midline on live pigs from 382 Beijing Black pigs. Gray gradient, gray level cooccurrence matrix and wavelet transform parameters whinin a defined region(80×80 pixel region across the 10th to 11th rib) for each ultrasound image were obtained using image analysis software (Matlab). After slaughter, a slice of longissimus muscle from left carcass across the 10th to 11th rib was cut off immediately for de-termining the intramuscular fat percentage (IMF) by the petroleum ether extraction method. The model to predict longissimus muscle intramuscular fat percentage (PIMF) was developed using linear regression analysis with carcass longissimus muscle intramuscular fat percentage (IMF) as dependent variables and body weight, backfat thickness, loin muscle area, loin muscle deepness and image parameters as independent variables. One hundred and twelve Beijing Black pigs were anew chose for model validation by correlation analysis of real intramuscular fat percentage and predicting intramuscular fat. The result of regression analysis indicated that 9 independent varia-bles containing backfat thickness, loin muscle area and 7 image parameters were significant (P< 0. 05) in last model. The coefficient of determination and root mean square error for the predic-tion model were 0. 305 8 and 0. 006 5. The correlation analysis showed that the Pearson Correla-tion Coefficients and Spearman Correlation Coefficients were 0. 553 4 and 0. 627 2 (P<0. 000 1). The result indicated that using real-time ultrasound image to predict intramuscular fat percentage in live pig was feasible. And this method can be used in Beijing Black pigs' breeding work and developing the intramuscular fat effectively.