Gérard
DAUMAS

ITP
– Le Rheu - France

**The reference for pig
classification is defined as a lean meat proportion. As this criteria is
destructive and very expensive to obtain it has to be predicted on slaughterline.
The EC regulations contain some requirements on how to predict the reference.
**

**Nevertheless, member
states have chosen in the past different ways for sampling and for parameters
estimation. Moreover, two issues have appeared : the reduction of experimental
costs and the management of large sets of highly correlated variables. These
difficulties have been solved by more complicated statistical methods.
**

**In order to help meat
scientists dealing with pig classification methods in the (present and future)
EU it has been decided to write some guidelines in a handbook. The first
official version (draft 3.1) has been distributed on February 2000 to the member
states delegates at a meeting of the Pigmeat Management Committee. Contributions
come from statisticians or meat scientists who are national expert for pig
classification. All are involved in EUPIGCLASS.
**

**The aim of WP2 is to
improve the first version of the statistical handbook, going further into
details, achieving a large consensus and illustrating with examples. We can
distinguish two parts : the statistical methods and sampling, because most of
the sampling considerations are general even if each method can have some
specificities.
**

**Some important issues
in sampling are : how to choose a representative sample (management of
sub-populations), how to select on predictors, which size ?
**

**All the statistical
methods derived from classical linear regression which was the initial method in
the EC regulation. They can be splitted into two groups :
**

**Methods for reducing experimental cost****Methods for managing large sets of highly correlated variables.**

**To cut the cost of
one experimental trial “double regression” has been first introduced in pig
classification by Engel & Walstra (1991). To reduce the costs of testing
different instruments Daumas & Dhorne (1997) have introduced “regression
with surrogate predictors”. These methods fulfill the present EC requirements
and estimation of accuracy is available.
**

**In parallel the Danes
have first introduced PCR (Principal Component Regression) to test the danish
robot Classification Centre (10-20 variables) and then PLS (Partial Least
Squares) to test the danish robot Autofom (> 100 variables). But no models
are behind these methods and the parameters estimation is under discussion.
**