Prediction of consumer palatability in beef using visual marbling scores and chemical intramuscular fat percentage.


Journal

Meat science
ISSN: 1873-4138
Titre abrégé: Meat Sci
Pays: England
ID NLM: 101160862

Informations de publication

Date de publication:
Nov 2021
Historique:
received: 15 05 2020
revised: 01 09 2020
accepted: 24 09 2020
pubmed: 18 10 2020
medline: 26 10 2021
entrez: 17 10 2020
Statut: ppublish

Résumé

With development of objective technologies that can predict chemical intramuscular fat percentage (IMF%), there is a need to understand the relationships between existing marbling traits, IMF% and eating quality. This study utilised historical carcass data (n = 9641 observations) from the Meat Standards Australia (MSA) industry research dataset and included MSA grading data, chemical IMF% data and weighted composite eating quality scores (MQ4). Several analyses were performed to assess the prediction of MQ4 by MSA marbling, M. longissimus thoracis et lumborum (striploin) IMF% and cut specific IMF%. Results demonstrated that there was similar precision between chemical IMF% (R

Identifiants

pubmed: 33067083
pii: S0309-1740(20)30754-3
doi: 10.1016/j.meatsci.2020.108322
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

108322

Informations de copyright

Copyright © 2020. Published by Elsevier Ltd.

Auteurs

S M Stewart (SM)

Advanced Livestock Measurement Technologies (ALMTech), Murdoch University, College of Science, Health and Engineering, Western Australia 6150, Australia. Electronic address: S.Stewart@murdoch.edu.au.

G E Gardner (GE)

Advanced Livestock Measurement Technologies (ALMTech), Murdoch University, College of Science, Health and Engineering, Western Australia 6150, Australia.

P McGilchrist (P)

Advanced Livestock Measurement Technologies (ALMTech), Murdoch University, College of Science, Health and Engineering, Western Australia 6150, Australia; University of New England, School of Environmental and Rural Science, New South Wales 2351, Australia.

D W Pethick (DW)

Advanced Livestock Measurement Technologies (ALMTech), Murdoch University, College of Science, Health and Engineering, Western Australia 6150, Australia.

R Polkinghorne (R)

University of New England, School of Environmental and Rural Science, New South Wales 2351, Australia; Birkenwood Pty. Ltd, 431 Timor Rd, Murrurundi, NSW, Australia.

J M Thompson (JM)

University of New England, School of Environmental and Rural Science, New South Wales 2351, Australia.

G Tarr (G)

The University of Sydney, School of Mathematics and Statistics, Sydney, New South Wales 2006, Australia.

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Classifications MeSH