Using multiple regression, neural networks and supprot vector machines to predict lamb carcasses composition
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abstract
The objective of this work was to use a Data Mining
(DM) approach to predict, using as predictors the carcass
measurements taken at slaughter line, the composition
of lamb carcasses. One hundred and twenty ve
lambs of Churra Galega Bragan cana breed were slaughtered.
During carcasses quartering, a caliper was used to
perform subcutaneous fat measurements, over the maximum
depth of longissimus muscle (LM), between the
12th and 13th ribs (C12), and between the 1st and 2nd
lumbar vertebrae (C1). The Muscle (MP), Bone (BP),
Subcutaneous Fat (SFP), Inter-Muscular Fat (IFP), and
Kidney Knob and Channel Fat (KKCF) proportions of
lamb carcasses were computed. We used the rminer R
library and compared three regression techniques: Multiple
Regression (MR), Neural Networks (NN) and Support
Vector Machines (SVM). The SVM model provided
the lowest relative absolute error for the prediction of
BP, SFP and KKCF, while MR presented the best predictions
for MP and IFP. Also, a sensitivity analysis
procedure revealed the C12 measurement as the most
relevant predictor for all ve carcass tissues.
