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Ultra-wideband microwave precisely and accurately predicts sheepmeat hot carcase GR tissue depth.
Marimuthu, J; Loudon, K M W; Karayakallile Abraham, R; Pamarla, V; Gardner, G E.
Afiliação
  • Marimuthu J; School of Agricultural Sciences, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, WA 6150, Australia; Advanced Livestock Measurement Technologies project, Meat and Livestock Australia, NSW 2060, Australia.
  • Loudon KMW; School of Agricultural Sciences, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, WA 6150, Australia; Advanced Livestock Measurement Technologies project, Meat and Livestock Australia, NSW 2060, Australia. Electronic address: K.Loudon@murdoch.edu.au.
  • Karayakallile Abraham R; School of Agricultural Sciences, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, WA 6150, Australia; Advanced Livestock Measurement Technologies project, Meat and Livestock Australia, NSW 2060, Australia.
  • Pamarla V; School of Agricultural Sciences, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, WA 6150, Australia; Advanced Livestock Measurement Technologies project, Meat and Livestock Australia, NSW 2060, Australia.
  • Gardner GE; School of Agricultural Sciences, Centre for Animal Production and Health, Food Futures Institute, Murdoch University, WA 6150, Australia; Advanced Livestock Measurement Technologies project, Meat and Livestock Australia, NSW 2060, Australia.
Meat Sci ; 217: 109623, 2024 Nov.
Article em En | MEDLINE | ID: mdl-39141967
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Carneiro Doméstico / Carne Vermelha / Micro-Ondas Limite: Animals Idioma: En Revista: Meat Sci Assunto da revista: CIENCIAS DA NUTRICAO Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Austrália País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Carneiro Doméstico / Carne Vermelha / Micro-Ondas Limite: Animals Idioma: En Revista: Meat Sci Assunto da revista: CIENCIAS DA NUTRICAO Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Austrália País de publicação: Reino Unido