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Untrained consumer assessment of the eating quality of beef: 1. A single composite score can predict beef quality grades

Published online by Cambridge University Press:  10 November 2016

S. P. F. Bonny
Affiliation:
School of Veterinary and Life Sciences, Murdoch University, Murdoch, WA 6150, Australia INRA, UMR1213, Recherches sur les Herbivores, F-63122 Saint Genès Champanelle, France
J.-F. Hocquette
Affiliation:
INRA, UMR1213, Recherches sur les Herbivores, F-63122 Saint Genès Champanelle, France Clermont Université, VetAgro Sup, UMR1213, Recherches sur les Herbivores, F-63122 Saint Genès Champanelle, France
D. W. Pethick
Affiliation:
School of Veterinary and Life Sciences, Murdoch University, Murdoch, WA 6150, Australia
I. Legrand
Affiliation:
Institut de l’Elevage, Service Qualite’ des Viandes, MRAL, 87060 Limoges Cedex 2, France
J. Wierzbicki
Affiliation:
Polish Beef Association Ul, Kruczkowskiego 3, 00-380 Warszawa, Poland
P. Allen
Affiliation:
Teagasac Food Research Centre, Ashtown, Dublin 15, Ireland
L. J. Farmer
Affiliation:
Agri-Food and Biosciences Institute, Newforge Lane, Belfast BT9 5PX, UK
R. J. Polkinghorne
Affiliation:
431 Timor Road, Murrurundi, NSW 2338, Australia
G. E. Gardner
Affiliation:
School of Veterinary and Life Sciences, Murdoch University, Murdoch, WA 6150, Australia
Corresponding
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Abstract

Quantifying consumer responses to beef across a broad range of demographics, nationalities and cooking methods is vitally important for any system evaluating beef eating quality. On the basis of previous work, it was expected that consumer scores would be highly accurate in determining quality grades for beef, thereby providing evidence that such a technique could be used to form the basis of and eating quality grading system for beef. Following the Australian MSA (Meat Standards Australia) testing protocols, over 19 000 consumers from Northern Ireland, Poland, Ireland, France and Australia tasted cooked beef samples, then allocated them to a quality grade; unsatisfactory, good-every-day, better-than-every-day and premium. The consumers also scored beef samples for tenderness, juiciness, flavour-liking and overall-liking. The beef was sourced from all countries involved in the study and cooked by four different cooking methods and to three different degrees of doneness, with each experimental group in the study consisting of a single cooking doneness within a cooking method for each country. For each experimental group, and for the data set as a whole, a linear discriminant function was calculated, using the four sensory scores which were used to predict the quality grade. This process was repeated using two conglomerate scores which are derived from weighting and combining the consumer sensory scores for tenderness, juiciness, flavour-liking and overall-liking, the original meat quality 4 score (oMQ4) (0.4, 0.1, 0.2, 0.3) and current meat quality 4 score (cMQ4) (0.3, 0.1, 0.3, 0.3). From the results of these analyses, the optimal weightings of the sensory scores to generate an ‘ideal meat quality 4 score (MQ4)’ for each country were calculated, and the MQ4 values that reflected the boundaries between the four quality grades were determined. The oMQ4 weightings were far more accurate in categorising European meat samples than the cMQ4 weightings, highlighting that tenderness is more important than flavour to the consumer when determining quality. The accuracy of the discriminant analysis to predict the consumer scored quality grades was similar across all consumer groups, 68%, and similar to previously reported values. These results demonstrate that this technique, as used in the MSA system, could be used to predict consumer assessment of beef eating quality and therefore to underpin a commercial eating quality guarantee for all European consumers.

Type
Research Article
Information
animal , Volume 11 , Issue 8 , August 2017 , pp. 1389 - 1398
Copyright
© The Animal Consortium 2016 

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