Barducci Robson S, Zhou Ziyu Y, Wormsbecher Lisa, Roehrig Colleen, Tulpan Dan, Bohrer Benjamin M
Department of Food Science, University of Guelph, Guelph, Ontario, Canada.
Conestoga Meat Packers Ltd., Breslau, Ontario, Canada.
Transl Anim Sci. 2019 Oct 23;4(1):331-338. doi: 10.1093/tas/txz169. eCollection 2020 Jan.
This study aimed to examine the correlation of carcass weight, fat depth, muscle depth, and predicted lean yield in commercial pigs. Data were collected on 850,819 pork carcasses from the same pork processing facility between October 2017 and September 2018. Hot carcass weight was reported following slaughter as a head-on weight; while fat and muscle depth were measured with a Destron PG-100 probe and used for the calculation of predicted lean yield based on the Canadian Lean Yield () equation [CLY (%) = 68.1863 - (0.7833 × fat depth) + (0.0689 × muscle depth) + (0.0080 × fat depth) - (0.0002 × muscle depth) + (0.0006 × fat depth × muscle depth)]. Descriptive statistics, regression equations including coefficients of determination, and Pearson product moment correlation coefficients (when assumptions for linearity were met) and Spearman's rank-order correlation coefficients (when assumptions for linearity were not met) were calculated for attributes using SigmaPlot, version 11 (Systat Software, Inc., San Jose, CA). Weak positive correlation was observed between hot carcass weight and fat depth ( = 0.289; < 0.0001), and between hot carcass weight and muscle depth ( = 0.176; < 0.0001). Weak negative correlations were observed between hot carcass weight and predicted lean yield ( = -0.235; < 0.0001), and between fat depth and muscle depth ( = -0.148; < 0.0001). Upon investigation of relationships between fat depth and predicted lean yield, and between muscle depth and predicted lean yield using scatter plots, it was determined that these relationships were not linear and therefore the assumptions of Pearson product moment correlation were not met. Thus, these relationships were expressed as nonlinear functions and Spearman's rank-order correlation coefficients were used. A strong negative correlation was observed between fat depth and predicted lean yield ( = -0.960; < 0.0001), and a moderate positive correlation was observed between muscle depth and predicted lean yield ( = 0.406; < 0.0001). Results from this dataset revealed that hot carcass weight was generally weakly correlated ( < |0.35|) with fat depth, muscle depth, and predicted lean yield. Therefore, it was concluded that there were no consistent weight thresholds where pigs were fatter or heavier muscled.
本研究旨在检验商品猪胴体重量、脂肪厚度、肌肉厚度和预测瘦肉产量之间的相关性。收集了2017年10月至2018年9月间来自同一猪肉加工设施的850819头猪胴体的数据。屠宰后报告的热胴体重量为正面重量；脂肪和肌肉厚度用Destron PG - 100探头测量，并根据加拿大瘦肉产量（）方程[CLY（%）= 68.1863 - （0.7833×脂肪厚度）+（0.0689×肌肉厚度）+（0.0080×脂肪厚度） - （0.0002×肌肉厚度）+（0.0006×脂肪厚度×肌肉厚度）]用于计算预测瘦肉产量。使用SigmaPlot 11版（Systat Software公司，加利福尼亚州圣何塞）计算属性的描述性统计、包括决定系数的回归方程以及Pearson积矩相关系数（当满足线性假设时）和Spearman等级相关系数（当不满足线性假设时）。热胴体重量与脂肪厚度之间观察到弱正相关（ = 0.289； < 0.0001），热胴体重量与肌肉厚度之间也观察到弱正相关（ = 0.176； < 0.0001）。热胴体重量与预测瘦肉产量之间观察到弱负相关（ = -0.235； < 0.0001），脂肪厚度与肌肉厚度之间观察到弱负相关（ = -0.148； < 0.0001）。通过散点图研究脂肪厚度与预测瘦肉产量之间以及肌肉厚度与预测瘦肉产量之间的关系后，确定这些关系不是线性的，因此不满足Pearson积矩相关的假设。因此，这些关系表示为非线性函数并使用Spearman等级相关系数。脂肪厚度与预测瘦肉产量之间观察到强负相关（ = -0.960； < 0.0001），肌肉厚度与预测瘦肉产量之间观察到中度正相关（ = 0.406； < 0.0001）。该数据集的结果表明，热胴体重量通常与脂肪厚度、肌肉厚度和预测瘦肉产量弱相关（ < |0.35|）。因此，得出的结论是，不存在猪更肥或肌肉更发达的一致体重阈值。
