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Estimation of dairy goat body composition: A direct calibration and comparison of eight methods
Methods ( IF 4.2 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.ymeth.2020.06.014
Sylvain Lerch 1 , Anne De La Torre 2 , Christophe Huau 3 , Mathieu Monziols 4 , Caroline Xavier 5 , Loïc Louis 6 , Yannick Le Cozler 7 , Philippe Faverdin 7 , Philippe Lamberton 7 , Isabelle Chery 8 , Dominique Heimo 9 , Christelle Loncke 10 , Philippe Schmidely 10 , José A A Pires 2
Affiliation  

The objective was to compare eight methods for estimation of dairy goat body composition, by calibrating against chemical composition (water, lipid, protein, mineral and energy) measured post-mortem. The methods tested on 20 Alpine goats were body condition score (BCS), 3-dimension imaging (3D) automatic assessment of BCS or whole body scan, ultrasound, computer tomography (CT), adipose cell diameter, deuterium oxide dilution space (D2OS) and bioelectrical impedance spectroscopy (BIS). Regressions were tested between predictive variates derived from the methods tested and empty body (EB) composition. The best equations for estimation of EB lipid mass included BW combined with i) perirenal adipose tissue mass and cell diameter (R2=0.95, residual standard deviation, rSD=0.57 kg), ii) volume of fatty tissues measured by CT (R2=0.92, rSD=0.76 kg), iii) D2OS (R2=0.91, rSD=0.85 kg), and iv) resistance at infinite frequency from BIS (R2=0.87, rSD=1.09 kg). The D2OS combined with BW provided the best equation for EB protein mass (R2=0.97, rSD=0.17 kg), whereas BW alone provided a fair estimate (R2=0.92, rSD=0.25 kg). Sternal BCS combined with BW provided good estimation of EB lipid and protein mass (R2=0.80 and 0.95, rSD=1.27 and 0.22 kg, respectively). Compared to manual BCS, BCS by 3D slightly decreased the precision of the predictive equation for EB lipid (R2=0.74, rSD=1.46 kg), and did not improve the estimation of EB protein compared with BW alone. Ultrasound measurements and whole body 3D imaging methods were not satisfactory estimators of body composition (R2≤0.40). Further developments in body composition techniques may contribute for high-throughput phenotyping of robustness.

中文翻译:

奶山羊身体成分的估计:八种方法的直接校准和比较

目的是通过对死后测量的化学成分(水、脂质、蛋白质、矿物质和能量)进行校准,比较八种估计奶山羊身体成分的方法。在 20 只高山山羊上测试的方法是身体状况评分 (BCS)、BCS 的 3 维成像 (3D) 自动评估或全身扫描、超声、计算机断层扫描 (CT)、脂肪细胞直径、氧化氘稀释空间 (D2OS)和生物电阻抗谱 (BIS)。测试了从测试方法和空体 (EB) 组成得出的预测变量之间的回归。估计 EB 脂质质量的最佳方程包括 BW 结合 i) 肾周脂肪组织质量和细胞直径(R2=0.95,残留标准偏差,rSD=0.57 kg),ii)通过 CT 测量的脂肪组织体积(R2=0.92 , rSD=0.76 公斤), iii) D2OS (R2=0.91, rSD=0.85 kg),以及 iv) 来自 BIS 的无限频率电阻(R2=0.87, rSD=1.09 kg)。D2OS 与 BW 相结合提供了 EB 蛋白质质量的最佳方程(R2=0.97,rSD=0.17 kg),而 BW 单独提供了一个公平的估计(R2=0.92,rSD=0.25 kg)。胸骨 BCS 结合 BW 提供了对 EB 脂质和蛋白质质量的良好估计(分别为 R2=0.80 和 0.95,rSD=1.27 和 0.22 kg)。与手动 BCS 相比,3D 的 BCS 略微降低了 EB 脂质预测方程的精度(R2 = 0.74,rSD = 1.46 kg),并且与单独的 BW 相比并没有改善 EB 蛋白的估计。超声测量和全身 3D 成像方法不能令人满意地估计身体成分 (R2≤0.40)。身体成分技术的进一步发展可能有助于稳健性的高通量表型分析。
更新日期:2021-02-01
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