L.EQT.1720. Using Consumer Sensory Testing to further enhance MSA beef model expansion and accuracy L.EQT.1809 Consumer sensory evaluation of stored product

Summary

​The Meat Standards Australia (MSA) prediction model evaluates consumer response to determine the quality of different cuts of meat by different cook methods.

The two projects outlined in this report involved extensive consumer testing of sensory samples, prepared from several previously researched meat collections, to expand and increase the accuracy of the current MSA prediction model.

The results of the research have dramatically expanded the predication capability of Meat Standards Australia, and represent a substantial step towards predicting consumer satisfaction for any beef carcase portion cooked by alternative methods.

Objectives

The two projects aimed to:

• improve the MSA model accuracy on cuts with limited information
  expand the MSA model with new cut by cook combinations and cooking methods
• test existing cooking methods to determine if consumer sensory preferences have changed.

Key findings

• By testing 7,770 samples with 11,100 consumers, the projects have added considerable data to the MSA prediction model.
• The data, in conjunction with other recent research, has doubled the number of the cut by cook combination predictions within the new MSA model.
• New cooking methods have been established, including sous vide of casserole beef, combi oven high moisture roasting, flatiron preparation of oyster blade and bone in versus boneless cooking forms for rib and shin (osso bucco) cuts.

Benefits to industry

The data collected during the two projects is central to the considerable expansion of the MSA prediction model, with the new version encompassing more than double the cut by cook combinations relative to the old prediction model.

MLA action

The 2019 V2.0 MSA model version has been developed based off the findings of this project and is due for release and implementation by 30 June 2020.

Future research

Efforts should continue to encourage and facilitate the pooling of national and international data to further empower the prediction accuracy of the MSA model.