  In chemometrics, data from infrared or near-infrared (NIR) spectroscopy are
often used to identify a compound or to analyze the composition of amaterial.
This involves the calibration of models that predict the concentration
ofmaterial constituents from the measured NIR spectrum. An interesting aspect
of multivariate calibration is to achieve a particular accuracy level with a
minimum number of training samples, as this reduces the number of laboratory
tests and thus the cost of model building. In these chemometric models, the
input refers to a proper representation of the spectra and the output to the
concentrations of the sample constituents. The search for a most informative
new calibration sample thus has to be performed in the output space of the
model, rather than in the input space as in conventionalmodeling problems. In
this paper, we propose to solve the corresponding inversion problem by
utilizing the disagreements of an ensemble of neural networks to represent the
prediction error in the unexplored component space. The next calibration sample
is then chosen at a composition where the individual models of the ensemble
disagree most. The results obtained for a realistic chemometric calibration
example show that the proposed active learning can achieve a given calibration
accuracy with less training samples than random sampling.
