Journal article
Prediction of consumer palatability in beef using visual marbling scores and chemical intramuscular fat percentage
Meat Science, Vol.181, Article 108322
2021
Abstract
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% (R2 = 0.32, RSE = 11.8) and MSA marbling (R2 = 0.28, RSE = 11.9) in the prediction of grilled 14 day aged striploin MQ4, with similar results across other cut by cook by days aged combinations. These results support the development of objective technologies that predict chemical IMF% in parallel with MSA marbling for carcass grading and the prediction of eating quality.
Details
- Title
- Prediction of consumer palatability in beef using visual marbling scores and chemical intramuscular fat percentage
- Authors/Creators
- S.M. Stewart (Author/Creator) - Murdoch UniversityG.E. Gardner (Author/Creator) - Murdoch UniversityP. McGilchrist (Author/Creator) - University of New EnglandD.W. Pethick (Author/Creator) - Murdoch UniversityR. Polkinghorne (Author/Creator) - University of New EnglandJ.M. Thompson (Author/Creator) - University of New EnglandG. Tarr (Author/Creator) - The University of Sydney
- Publication Details
- Meat Science, Vol.181, Article 108322
- Publisher
- Elsevier
- Identifiers
- 991005545559207891
- Copyright
- © 2020 Elsevier Ltd
- Murdoch Affiliation
- School of Agricultural Sciences; Food Futures Institute
- Language
- English
- Resource Type
- Journal article
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InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.51 Dairy & Animal Sciences
- 3.51.206 Meat Quality
- Web Of Science research areas
- Food Science & Technology
- ESI research areas
- Agricultural Sciences