Please use this identifier to cite or link to this item:
|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||DOI:||10.1071/EA07184||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:||Melbourne, Victoria, Australia||ISSN:||0816-1089||Field of Research (FOR):||070299 Animal Production not elsewhere classified||Peer Reviewed:||Yes||HERDC Category Description:||C1 Refereed Article in a Scholarly Journal||Other Links:||http://nla.gov.au/anbd.bib-an4599774||Statistics to Oct 2018:||Visitors: 149
|Appears in Collections:||Journal Article|
Files in This Item:
checked on Nov 26, 2018
checked on Mar 3, 2019
Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.