Ultra-wideband microwave precisely and accurately predicts sheepmeat hot carcase GR tissue depth

IF 7.1 1区 农林科学 Q1 Agricultural and Biological Sciences Meat Science Pub Date : 2024-08-05 DOI:10.1016/j.meatsci.2024.109623
J. Marimuthu , K.M.W. Loudon , R. Karayakallile Abraham , V. Pamarla , G.E. Gardner
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Abstract

A portable ultra-wideband microwave system (MiS) coupled with an antipodal slot Vivaldi patch antenna (VPA) was used as an objective measurement technology to predict sheep meat carcase GR tissue depth, tested against AUS-MEAT national accreditation standards. Experiment one developed the MiS GR tissue depth prediction equation using lamb carcasses (n = 832) from two slaughter groups. To create the prediction equations, a two layered machine learning stacking ensemble technique was used. The performance of this equation was tested within the dataset using a k-fold cross validation (k = 5), which demonstrated excellent precision and accuracy with an average R2 of 0.91, RMSEP 2.11, bias 0.39 and slope 0.03. Experiment two tested the prediction equation against the AUS-MEAT GR tissue depth accreditation framework which stipulates predictions from a device must assign the correct fat score, with a tolerance of ±2 mm of the score boundary, and 90% accuracy. For a device to be accredited three measurements captured within the same device, as well as measurements across three different devices, must meet the AUS-MEAT error thresholds. Three MiS devices scanned lamb carcases (n = 312) across three slaughter days. All three MiS devices met the AUS-MEAT accreditation thresholds, accurately predicting GR tissue depth 96.1–98.4% of the time. Between the different devices, the measurement accuracy was 99.4–100%, and within the same device, the measurement accuracy was 99.7–100%. Based on these results MiS achieved AUS-MEAT device accreditation as an objective technology to predict GR tissue depth.

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超宽带微波精确预测羊肉热胴体 GR 组织深度
将便携式超宽带微波系统(MiS)与反角槽维瓦尔第贴片天线(VPA)作为一种客观测量技术,用于预测绵羊肉胴体GR组织深度,并根据澳大利亚-MEAT国家认证标准进行了测试。实验一使用来自两个屠宰组的羔羊胴体(n = 832)建立了 MiS GR 组织深度预测方程。为了建立预测方程,使用了双层机器学习堆叠集合技术。在数据集中使用 k 倍交叉验证(k = 5)对该方程的性能进行了测试,结果表明该方程具有出色的精确度和准确度,平均 R2 为 0.91,RMSEP 为 2.11,偏差为 0.39,斜率为 0.03。实验二根据 AUS-MEAT GR 组织深度认证框架对预测方程进行了测试,该框架规定设备的预测必须分配正确的脂肪分数,分数边界的误差为 ±2 毫米,准确率为 90%。一台设备要获得认证,必须在同一台设备上进行三次测量,以及在三台不同设备上进行测量,都必须达到 AUS-MEAT 误差阈值。三台 MiS 设备在三个屠宰日对羔羊尸体(n = 312)进行扫描。所有三台 MiS 设备都达到了 AUS-MEAT 认证阈值,在 96.1-98.4% 的时间内准确预测了 GR 组织深度。不同设备之间的测量准确率为 99.4-100%,同一设备内部的测量准确率为 99.7-100%。基于这些结果,MiS 获得了 AUS-MEAT 设备认证,成为预测 GR 组织深度的客观技术。
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来源期刊
Meat Science
Meat Science 工程技术-食品科技
CiteScore
12.60
自引率
9.90%
发文量
282
审稿时长
60 days
期刊介绍: The aim of Meat Science is to serve as a suitable platform for the dissemination of interdisciplinary and international knowledge on all factors influencing the properties of meat. While the journal primarily focuses on the flesh of mammals, contributions related to poultry will be considered if they enhance the overall understanding of the relationship between muscle nature and meat quality post mortem. Additionally, papers on large birds (e.g., emus, ostriches) as well as wild-captured mammals and crocodiles will be welcomed.
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