Maria
Font i Furnols

IRTA-Meat
Technology Centre. Granja Camps i Armet. E-17121 Monells (Girona)

Lean
meat percentage (LMP) is a parameter of pig carcass classification in the
European Union and it is obtained by means of measurements with different kinds
of automatic probes, that have been previously calibrated. To obtain the
calibration equation the European Commission has developed some rules (Commission
Regulation (EEC) Nš 2967/85 and its modification Nš 3127/94), that establish
the number of carcasses to be dissected (120) and the fact that these have to be
representative of the national or regional pig meat production. At the same
time, the root mean square deviation of the errors (RMSE), measured about zero,
has to be less than 2.5. Nevertheless the selection method has not been
specified in the regulation and it can be done in different ways. The aim of
this paper was to study, by means of resampling methods, the influence of the
selection method on the regression parameters.

The
lean meat percentage (*LMP*) was
obtained by dissection and predicted by means of regression method in which the
independent variables were fat thickness (*g34fom*)
and the lean depth (*m34fom*) measured
between the 3^{rd} and the 4^{th} last ribs, at 60 mm from the
mid-line with Fat-o-Meter. The methods studied were *random*
selection and three other selection methods depending on the percentage of
samples selected in the group with low (m-s),
medium and high (m+s)
level of *g34fom*: 16%-68%-16% or normal distribution (*se166816*), 33%-33%-33% (*se333333*)
and 40%-20%-40% (se*402040*). The data
was divided into two sets of data that follow a normal distribution. One set of
data is used for the calibration equation, and from this set different
subsamples (n=120) were selected according to the four different selection
methods by means of the *surveyselect*
procedure of SAS software. From these subsamples the parameters of the
regression equation were calculated. The validation set was used to obtain the
root mean standard prediction error (RMSPE). This procedure was repeated 20
times.

**Unimportant differences were detected in the distribution of the RMSE for
the different sampling methods. The distribution of the s.e. of the different
regression parameters showed the main differences, the s.e. of the g34fom
being the most discriminant. Sampling with weighted extremes (se333333
and se402040) had the distribution
with lower values, se402040 being the
lowest. When the distribution of the RMSPE was evaluated unimportant differences
can be seen depending on the selection method. When only 5% of the most extreme
carcasses were taken into account, the RMSPE of the se333333
and se402040 had a tendency to have less higher values.**

The
influence of the sampling method in the standard error of the slope of the
regression variables has been demonstrated from a practical point of view. The
expected influence of the sampling method in the RMSPE, when only 5% of the most
extreme carcasses were taken into account, cannot be demonstrated. It is
probably due to the fact that the most extreme carcasses in the data set were
not extreme enough.