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

Robust variable selection in the framework of classification with label noise and outliers: applications to spectroscopic data in agri-food