  Data from spectrophotometers form vectors of a large number of exploitable
variables. Building quantitative models using these variables most often
requires using a smaller set of variables than the initial one. Indeed, a too
large number of input variables to a model results in a too large number of
parameters, leading to overfitting and poor generalization abilities. In this
paper, we suggest the use of the mutual information measure to select variables
from the initial set. The mutual information measures the information content
in input variables with respect to the model output, without making any
assumption on the model that will be used; it is thus suitable for nonlinear
modelling. In addition, it leads to the selection of variables among the
initial set, and not to linear or nonlinear combinations of them. Without
decreasing the model performances compared to other variable projection
methods, it allows therefore a greater interpretability of the results.
