Intramuscular fat (IMF) contributes significantly to the aroma and tenderness of the meat, therefore playing a key role in quality determination. Yet, IMF determination methods rely on visual inspection or on fat extraction from meat samples after animals' slaughter. The aim of this methodological study was the elaboration of a process capable of predicting IMF% using real-time ultrasound (RTU) images in live beef cattle. The longissimus dorsi (LD) muscle of 26 Charolaise heifers was investigated. In vivo ultrasound images were taken and texture analysis was performed. One week after the animals' slaughter, the whole twelfth rib cut was collected, and IMF% was determined by extraction with petrol ether (Randall) method. Animals were divided in 3 groups depending on their mean lipid content percentage in 100 g meat (Group 1: IMF ≤ 4.24%; Group 2: 4.25% ≤ IMF ≤ 5.75%; Group 3: IMF ≥ 5.76%). Texture parameters were selected by a stepwise linear discriminant analysis using IMF% measured by chemical extraction (IMFqa) as the dependent variable, and the results of the texture analysis as explanatory variables. 6 variables were found predictive and molded into a multiple regression equation, this equation was then validated using IMFqa as ground truth. A high linear correlation between IMFqa and IMFpred was evident (r2 = 0.8504), ROC analysis perfomed on IMFpred comparing it to IMFqa showed a sensitivity of 80% and a specificity of 93.7%, while results from the Bland-Altman plot were ± 1.96 (±1.11SD).

中文翻译：

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b型超声图像纹理分析在活体肉牛肌内脂肪定量和预测中的应用：方法学研究。

肌内脂肪（IMF）显着促进了肉的香气和嫩度，因此在质量确定中起着关键作用。然而，IMF的确定方法依靠肉眼检查或动物屠宰后从肉类样品中提取脂肪。这项方法学研究的目的是阐述一种能够使用实时超声（RTU）图像预测活牛牛中IMF％的过程。研究了26个夏洛来牛小母牛的背最长肌（LD）。拍摄体内超声图像并进行纹理分析。宰杀动物一周后，收集完整的第十二条肋骨，并通过汽油醚（Randall）萃取法测定IMF％。根据动物在100克肉中的平均脂质含量百分比将其分为3组（组1：IMF≤4.24％；第2组：4.25％≤IMF≤5.75％；第3组：IMF≥5.76％）。通过逐步线性判别分析，使用化学提取法测得的IMF％（IMFqa）作为因变量，选择纹理参数，并将纹理分析的结果作为解释变量。发现6个变量具有预测性，并成型为一个多元回归方程，然后使用IMFqa作为基础事实对该方程进行了验证。IMFqa和IMFpred之间存在高度线性相关性（r2 = 0.8504），在IMFpred上进行的ROC分析与IMFqa进行的ROC分析显示，灵敏度为80％，特异性为93.7％，而Bland-Altman图的结果为±1.96（ ±1.11SD）。通过逐步线性判别分析，使用化学提取法测得的IMF％（IMFqa）作为因变量，选择纹理参数，并将纹理分析的结果作为解释变量。发现6个变量具有预测性，并成型为一个多元回归方程，然后使用IMFqa作为基础事实对该方程进行了验证。IMFqa和IMFpred之间存在高度线性相关性（r2 = 0.8504），在IMFpred上进行的ROC分析与IMFqa进行的ROC分析显示，灵敏度为80％，特异性为93.7％，而Bland-Altman图的结果为±1.96（ ±1.11SD）。通过逐步线性判别分析，使用化学提取法测得的IMF％（IMFqa）作为因变量，选择纹理参数，并将纹理分析的结果作为解释变量。发现6个变量具有预测性，并成型为一个多元回归方程，然后使用IMFqa作为基础事实对该方程进行了验证。IMFqa和IMFpred之间存在高度线性相关性（r2 = 0.8504），在IMFpred上进行的ROC分析与IMFqa进行的ROC分析显示，灵敏度为80％，特异性为93.7％，而Bland-Altman图的结果为±1.96（ ±1.11SD）。