Report Detail Page

Sheep Meat Eating Quality

This work was carried out to extend earlier results on sheep meat eating quality to a range of commercial meat cuts, different methods of cooking (grill, roast, stir fry and slow wet cook) and to examine a wider range of consumer responses. The goal was to classify the eating quality of commercial sheep meat cuts and cooking methods in a manner that could be presented simply to both consumers and participants in the sheep meat supply chain. The outcome of this work is a simple, colour coded easy to understand chart to guide consumer choice based on the frequency with which meat of a given type would fail to meet expectations.

Data on consumer evaluation of the sensory variables (smell, tender, juicy, flavour and overall liking) were collected on 14,280 consumer responses. Each consumer was also asked to place the meat sampling in 1 of 5 categories where 1 represented very unsatisfactory eating quality and 5 represented excellent eating quality.

The analysis proceeded in 2 stages. First the relationships among the sensory variables were analysed to link the consumer responses to measurable quantities. Then these relationships were linked to the consumers judgement of eating quality and used to classify the various animal category x cut type x cooking method combinations in terms of the expected eating quality.

Overall liking of a meat sample was strongly associated with the perceived flavour in all cases. Tender and juicy had minor roles in discriminating between overall liking, while smell tended to be associated with flavour and did not show any independent relationship. There were no practical differences in these relationships between cut type or cooking method. The residual variation within cut type and cooking method was the same. Because of this the overall liking score alone provided the best measurement of the meat sample desirability. That is, there was no improvement in using including any other sensory variables in an index (e.g. a principal component) as a measurement of meat quality. Thus, overall liking alone was chosen as the meat quality measure.

The results of the analysis of the sensory variables can be summerised as follows:

1. Meat from lambs and yearlings is more desirable than meat from ewes.

2. The ranking of the desirability of the cuts over each cooking method was similar between animal category.

3. In all animal categories the relationship with smell is small.

4. Tender is more important in determining desirability in ewes than in lambs and yearlings.

5. Juicy is more important in roasts and stir fry than in grills.

6. If the goal is predictability of overall liking from the other sensory variables then within cooking method and animal category one equation relating overall liking to the other sensory variables can be used for each cut.

While consumers rated the eating quality of the meat into 5 categories there was considerable overlap between categories 1 with 2 and 4 with 5. Combining these categories to produce a 3 point scale provided a much clearer classification, so the 3 point scale was adopted.

The most important characteristic of the consumer judgement of sheep meat eating quality is the variability of the responses. This variability is a natural property of the population of interest, and any scheme to promote sheep meat eating quality must recognise the extent of this variation and include it within any management program. That is, people perceive taste/quality differently, and it is impossible to have a piece of meat where 100% of the people will find satisfactory. The concept of failure rate was developed in the previous MLA SMEQ project to address this issue. Failure rate measures the proportion of people that will find a piece of meat to be of unsatisfactory eating quality. That is the failure rate is the area under the frequency distribution of the meat quality measure (overall liking) representing the proportion of consumers expected to deem the meat of unsatisfactory eating quality. In this case unsatisfactory meant consumers classifying the meat into category of the 3 point scale. To implement the failure rate classification it was necessary to calculate the appropriate frequency distributions for overall liking for each cut x cook category.

Analysis showed that while the means of overall liking differed between animal category x cut x cook the variances only differed significantly between lambs or yearlings and ewes. Thus there could be 1 form of residual distribution for lambs and yearlings and one for ewes. However, the residual distribution for lambs and yearlings was not Normal – there was significant kurtosis. Therefore a particular form for this distribution had to be calculated. The residual distribution for ewes was normal. Using these calculations the failure rates for each animal category x cut type x cooking method could be calculated. These calculations form the basis of the results presented to industry in the form of a simple colour coded chart to guide the choice of sheep meat by a consumer.

Discriminant analysis provided the optimum partitioning of overall liking score into each of the 3 consumer eating quality categories. That is, the overall liking score below which meat was predicted to be unsatisfactory. Logit analysis provided equations for calculating the frequency (probability) with which meat of given overall liking would be deemed unsatisfactory eating quality. These estimates were used to construct the industry guide to sheep meat eating quality chart.

Analysis of consumer pairs showed that the repeatability of consumer judgements was low but positive (different consumers came to similar judgements of both the sensory variables and eating quality). The low repeatability was due to the high natural variance of these measurements referred to above.

There was little effect of GR fat or ultimate pH on the sensory variables or the meat eating quality.

Downloads

Title Size Date published
361.7KB 01/05/2006

This page was last updated on 10/11/2014

Join myMLA today

One username and password for key integrity and information Systems (LPA/NVD, NLIS, MSA & LDL).

A personalised online dashboard that provides news, weather, events and R&D tools relevant to you.

Customised market information and analysis.

Learn more about myMLA

myMLA Sign Up

Already registered for myMLA?

Sign in here