  Classification of high-dimensional spectroscopic data is a common task in
analytical chemistry. Well-established procedures like support vector machines
(SVMs) and partial least squares discriminant analysis (PLS-DA) are the most
common methods for tackling this supervised learning problem. Nonetheless,
interpretation of these models remains sometimes difficult, and solutions based
on feature selection are often adopted as they lead to the automatic
identification of the most informative wavelengths. Unfortunately, for some
delicate applications like food authenticity, mislabeled and adulterated
spectra occur both in the calibration and/or validation sets, with dramatic
effects on the model development, its prediction accuracy and robustness.
Motivated by these issues, the present paper proposes a robust model-based
method that simultaneously performs variable selection, outliers and label
noise detection. We demonstrate the effectiveness of our proposal in dealing
with three agri-food spectroscopic studies, where several forms of
perturbations are considered. Our approach succeeds in diminishing problem
complexity, identifying anomalous spectra and attaining competitive predictive
accuracy considering a very low number of selected wavelengths.
