scispace - formally typeset
R

Rod Polkinghorne

Researcher at University of Melbourne

Publications -  44
Citations -  1765

Rod Polkinghorne is an academic researcher from University of Melbourne. The author has contributed to research in topics: Tenderness & Willingness to pay. The author has an hindex of 20, co-authored 40 publications receiving 1476 citations.

Papers
More filters
Journal ArticleDOI

Consumer assessment of eating quality – development of protocols for Meat Standards Australia (MSA) testing

TL;DR: In this paper, the authors summarise the path taken to obtain consistent consumer assessment and present the MQ4 measure of consumer assessment of meat eating quality, which now forms the basis of the MSA predictive model.
Journal ArticleDOI

Meat standards and grading: a world view.

TL;DR: Future grading schemes which measure both carcass yield and eating quality have the potential to underpin the development and implementation of transparent value-based payment systems which will encourage improved production efficiency throughout the supply chain.
Journal ArticleDOI

Evolution of the Meat Standards Australia (MSA) beef grading system

TL;DR: In the early 1990s, the Australian Beef Industry identified variable eating quality as a major contributor to declining beef consumption and committed research funding to address the problem of the ability to predict the eating quality of cooked beef before consumption as discussed by the authors.
Journal ArticleDOI

Consumer assessment of beef strip loin steaks of varying fat levels.

TL;DR: Results of this study indicated that increased fat level in beef strip steaks positively affected tenderness, juiciness, flavor liking, and overall liking of beefstrip steaks.
Journal ArticleDOI

Development of the Meat Standards Australia (MSA) prediction model for beef palatability

TL;DR: In this article, the statistical aspects of the methodology that led to the Meat Standards Australia (MSA) prediction model for beef palatability are explained and described, which is used commercially to predict consumer satisfaction, in the form of a score out of 100, which in turn determines a grade outcome.