We investigate here the stability of the obtained results of a variable selection method recently introduced in the literature, and embedded into a model-based classification framework. It is applied to chemometric data, with the purpose of selecting a few wavenumbers (of the order of tens) among the thousands measured ones, to build a (robust) decision rule for classification. The robust nature of the method safeguards it from potential label noise and outliers, which are particularly dangerous in the field of food-authenticity studies. As a by-product of the learning process, samples are grouped into similar classes, and anomalous samples are also singled out. Our first results show that there is some variability around a common pattern in the obtained selection.

Cappozzo, A., Duponchel, L., Greselin, F., Murphy Thomas, B. (2021). Robust classification of spectroscopic data in agri-food: first analysis on the stability of results. In G.C. Porzio, C. Rampichini, C. Bocci (a cura di), ClaDAG 2021 Book of Abstracts and Short papers (pp. 49-52). Firenze University Press [10.36253/978-88-5518-340-6].

Robust classification of spectroscopic data in agri-food: first analysis on the stability of results

Cappozzo Andrea;Greselin Francesca;
2021

Abstract

We investigate here the stability of the obtained results of a variable selection method recently introduced in the literature, and embedded into a model-based classification framework. It is applied to chemometric data, with the purpose of selecting a few wavenumbers (of the order of tens) among the thousands measured ones, to build a (robust) decision rule for classification. The robust nature of the method safeguards it from potential label noise and outliers, which are particularly dangerous in the field of food-authenticity studies. As a by-product of the learning process, samples are grouped into similar classes, and anomalous samples are also singled out. Our first results show that there is some variability around a common pattern in the obtained selection.
Capitolo o saggio
Variable selection, Robust classification, Label noise, Outlier detection, Near-infrared spectroscopy, Mid-infrared spectroscopy, Agri-food.
English
ClaDAG 2021 Book of Abstracts and Short papers
978-88-5518-340-6
Cappozzo, A., Duponchel, L., Greselin, F., Murphy Thomas, B. (2021). Robust classification of spectroscopic data in agri-food: first analysis on the stability of results. In G.C. Porzio, C. Rampichini, C. Bocci (a cura di), ClaDAG 2021 Book of Abstracts and Short papers (pp. 49-52). Firenze University Press [10.36253/978-88-5518-340-6].
Cappozzo, A; Duponchel, L; Greselin, F; Murphy Thomas, B
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/335951
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