to many prediction variables by examples – PCR
15 years ago the Danish Meat Research Institute started to develop the first
full automatic classification equipment – the Classification Centre (CC). The
background for this rather ambitious project was a realization of the fact that
operators influence the measurements when they insert the probe using manual
solution was mechanic. The measurements were still made by insertion of probes,
but now automatically. Subsequent
to the classification, the carcass will automatically be branded with health
certificates and brands indicating the lean meat content (or SEUROP grade).
equipment consists of several mechanical items to fix the carcass and to measure
some characteristics of the carcass to position the probes. 7 probes are
inserted into the shoulder, back and ham and 7 measurements are made.
measurement consists of about 200 data, which describe how meat and fat reflect
light along a line of about 10 cm. The information is interpreted by use of a
neural network into 7 measurements of fat thickness and 3 measurements of the
next step is to validate the measurements and for this purpose the close
correlation between the measurements is used – from experience we know that
the thickness of fat vary almost in the same way along the carcass but at
different levels. If one thickness deviates improbably from the other
measurements, it will be replaced by a predicted thickness.
this sketch the different steps are shown. First, the measurements of length to
position the probes and after that the insertion of probes. The validation has
two actions. If too many deviations appear (i.e. 3 or more), the probes will be
pushed 2 cm upwards and inserted into the carcass again. This happens in about
10% of the cases. This part of the system is regarded as the equipment and the
10 results form the interface to the system we look at in our common Regulation
in the EU.
we have 10 results plus the weight. This contains all we know about the carcass
correlated to leanness. In the previous step – the validation step - we
utilized the correlation between the data. In the next step – the prediction
step - we want data with different information.
we use a so-called principal component analysis. The analysis can easily be
illustrated in two dimensions. When two variables are correlated they form an
we push the system of co-ordinates such that the origin is in the centre and the
x-axis along the main axis of the ellipse, then we can express each point by
independent variables. The new x-variable – the first principal component -
expresses the main part of the variation.
we do the same in 11 dimensions, we can reduce the number of variables to three
or four principal components, which contain the main part of the original
variation. These few independent variables are used to calibrate the equipment
by linear regression and finally, an equation for predicting the lean meat
percentage can be expressed by the original measurements.
have a very precise equipment and an equipment independent of the operators.
However, it discloses other dependencies.
lot of effort has been made to identify sources of error. The influence from the
knife mounted at the point of the probe and the influence from the slaughter
process (weak or strong surface treatment of the skin) are the most important.
system to monitor the measurements is necessary. The system has to include all
measurements and not only the meat percentage. If you have a problem, you have
to identify the reason for the problem, which can be one of the probes or one of
the items, which measure the length of the carcass etc.
economy - of both the pig producers and of the slaughterhouses - is very closely
related to the meat percentage. Every week the slaughterhouses compare their own
average results with others and they expect that the measurements are highly
reliable. Nationally, the trueness or the exact accordance with the reference
seems not to be the most important issue, but a possible bias between abattoirs
and the precision of the measurements is looked on as very important.