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Multi-class modelling for muscle level prediction of beef eating quality

Thursday, April 26, 2018 - 11:00am - 11:30am
Keller 3-180
Garth Tarr (University of Sydney)
The Meat Standards Australia (MSA) beef grading system was developed to improve the eating quality consistency of Australian beef for a range of muscles and cooking methods. The MSA system aims to predict the eating quality of beef given various inputs. Data has been collected and merged from experiments conducted over the past 20 years, resulting in a highly unbalanced data set where the majority of observations have been recorded on only a small subset of muscles with a far fewer observations on the majority of other muscles.

The current approach to building the MSA grading system is ad hoc and labour intensive. We present a new method of multi-class modelling using lasso-type penalties to encourage similar coefficient estimates that bridge a spectrum of muscle level models. At one extreme, each muscle and cook class is modelled independently and at the other extreme a single pooled regression model for all classes. An ideal model is somewhere between the two extremes, where classes with limited data are encouraged to borrow information from other classes.

We illustrate our method on real data and through simulation studies.
MSC Code: 
62J05