Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/2683
Title: Development of the Meat Standards Australia (MSA) prediction model for beef palatability
Contributor(s): Watson, R (author); Polkinghorne, R (author); Thompson, John Mitchell  (author)
Publication Date: 2008
Open Access: Yes
DOI: 10.1071/EA07184Open Access Link
Handle Link: https://hdl.handle.net/1959.11/2683
Abstract: In this paper, the statistical aspects of the methodology that led to the Meat Standards Australia (MSA) prediction model for beef palatability are explained and described. The model proposed here is descriptive: its intention is to describe the large amounts of data and MSA. The model is constrained to accord with accepted meat science principles. The combined dataset used in development of the prediction model reported is around 32000 rows x 140 columns. Each row represents a sample tasted by 10 consumers; each column specifies a variable relating to the sample tested. The developed model represents the interface between experimental data, scientific evaluation and commercial application. The model is used commercially to predict consumer satisfaction, in the form of a score out of 100, which in turn determines a grade outcome. An important improvement of the MSA model relative to other beef grading systems is that it assigns an individual consumer-based grade result to specific muscle portions cooked by designated methods; it does not assign a single grade to a carcass.
Publication Type: Journal Article
Source of Publication: Australian Journal of Experimental Agriculture, 48(11), p. 1368-1379
Publisher: CSIRO Publishing
Place of Publication: Australia
ISSN: 1446-5574
0816-1089
1836-5787
1836-0939
Fields of Research (FoR) 2008: 070299 Animal Production not elsewhere classified
Socio-Economic Objective (SEO) 2008: 830301 Beef Cattle
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Publisher/associated links: http://nla.gov.au/anbd.bib-an4599774
Appears in Collections:Journal Article

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