J. Marimuthu , K.M.W. Loudon , R. Karayakallile Abraham , V. Pamarla , G.E. Gardner
{"title":"Ultra-wideband microwave precisely and accurately predicts sheepmeat hot carcase GR tissue depth","authors":"J. Marimuthu , K.M.W. Loudon , R. Karayakallile Abraham , V. Pamarla , G.E. Gardner","doi":"10.1016/j.meatsci.2024.109623","DOIUrl":null,"url":null,"abstract":"<div><p>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 (<em>n</em> = 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 <em>R</em><sup>2</sup> 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 (<em>n</em> = 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.</p></div>","PeriodicalId":389,"journal":{"name":"Meat Science","volume":"217 ","pages":"Article 109623"},"PeriodicalIF":7.1000,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0309174024002006/pdfft?md5=ca454f380684f008df001c6dfab7457c&pid=1-s2.0-S0309174024002006-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Meat Science","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0309174024002006","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Agricultural and Biological Sciences","Score":null,"Total":0}
引用次数: 0
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.
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.