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比较一种先进的自动超声扫描仪(AutoFom III)和手持光学探头(Destron PG-100)在确定猪肉胴体瘦肉产量中的应用。

Comparison of an advanced automated ultrasonic scanner (AutoFom III) and a handheld optical probe (Destron PG-100) to determine lean yield in pork carcasses.

机构信息

Department of Food Science, University of Guelph, Guelph, ON N1G 2W1, Canada.

Conestoga Meat Packers Ltd., Breslau, ON N0B 1M0, Canada.

出版信息

J Anim Sci. 2023 Jan 3;101. doi: 10.1093/jas/skad058.

Abstract

This study compared the accuracy of two methods for predicting carcass leanness (i.e., predicted lean yield) with fat-free lean yields obtained by manual carcass side cut-out and dissection of lean, fat, and bone components. The two prediction methods evaluated in this study estimated lean yield by measuring fat thickness and muscle depth at one location with an optical grading probe (Destron PG-100) or by scanning the entire carcass with advanced ultrasound technology (AutoFom III). Pork carcasses (166 barrows and 171 gilts; head-on hot carcass weights (HCWs) ranging from 89.4 to 138.0 kg) were selected based on their fit within desired HCW ranges, their fit within specific backfat thickness ranges, and sex (barrow or gilt). Data (n = 337 carcasses) were analyzed using a 3 × 2 factorial arrangement in a randomized complete block design including the fixed effects of the method for predicting lean yield, sex, and their interaction, and random effects of producer (i.e., farm) and slaughter date. Linear regression analysis was then used to examine the accuracy of the Destron PG-100 and AutoFom III data for measuring backfat thickness, muscle depth, and predicted lean yield when compared with fat-free lean yields obtained with manual carcass side cut-outs and dissections. Partial least squares regression analysis was used to predict the measured traits from image parameters generated by the AutoFom III software. There were method differences (P < 0.01) for determining muscle depth and lean yield with no method differences (P = 0.27) for measuring backfat thickness. Both optical probe and ultrasound technologies strongly predicted backfat thickness (R2 ≥ 0.81) and lean yield (R2 ≥ 0.66), but poorly predicted muscle depth (R2 ≤ 0.33). The AutoFom III improved accuracy [R2 = 0.77, root mean square error (RMSE) = 1.82] for the determination of predicted lean yield vs. the Destron PG-100 (R2 = 0.66, RMSE = 2.22). The AutoFom III was also used to predict bone-in/boneless primal weights, which is not possible with the Destron PG-100. The cross-validated prediction accuracy for the prediction of primal weights ranged from 0.71 to 0.84 for bone-in cuts and 0.59 to 0.82 for boneless cut lean yield. The AutoFom III was moderately (r ≤ 0.67) accurate for the determination of predicted lean yield in the picnic, belly, and ham primal cuts and highly (r ≥ 0.68) accurate for the determination of predicted lean yield in the whole shoulder, butt, and loin primal cuts.

摘要

本研究比较了两种方法预测瘦肉率(即预测瘦肉产量)的准确性,方法是通过手动切割和解剖胴体的瘦肉、脂肪和骨骼成分来获得无脂瘦肉产量。本研究中评估的两种预测方法通过在一个位置使用光学分级探头(Destron PG-100)测量脂肪厚度和肌肉深度(n = 337 个胴体)或使用先进的超声技术(AutoFom III)扫描整个胴体来估计瘦肉产量。猪胴体(166 头公猪和 171 头母猪;去头热胴体重量(HCW)范围为 89.4 至 138.0 千克)是根据它们在理想 HCW 范围内的适应性、在特定背膘厚度范围内的适应性以及性别(公猪或母猪)进行选择的。数据采用随机完全区组设计的 3×2 因子排列进行分析,包括预测瘦肉产量的方法、性别及其相互作用的固定效应,以及生产者(即农场)和屠宰日期的随机效应。然后使用线性回归分析检查 Destron PG-100 和 AutoFom III 数据在测量背膘厚度、肌肉深度和预测瘦肉产量方面的准确性,与通过手动切割和解剖获得的无脂瘦肉产量进行比较。偏最小二乘回归分析用于根据 AutoFom III 软件生成的图像参数预测测量特征。两种方法在确定肌肉深度和瘦肉产量方面存在差异(P<0.01),但在测量背膘厚度方面没有差异(P=0.27)。光学探头和超声技术都能很好地预测背膘厚度(R2≥0.81)和瘦肉产量(R2≥0.66),但预测肌肉深度的效果较差(R2≤0.33)。AutoFom III 提高了预测瘦肉产量的准确性[R2=0.77,均方根误差(RMSE)=1.82],优于 Destron PG-100(R2=0.66,RMSE=2.22)。AutoFom III 还可用于预测带骨/无骨初级切块的重量,这是 Destron PG-100 无法实现的。带骨和无骨切块瘦肉产量预测的交叉验证预测准确性范围分别为 0.71 至 0.84 和 0.59 至 0.82。AutoFom III 对野餐、腹部和火腿初级切块的预测瘦肉产量的测定具有中等准确性(r≤0.67),对整个肩部、臀部和腰部初级切块的预测瘦肉产量的测定具有高度准确性(r≥0.68)。

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