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

Andrea Cappozzo, Ludovic Duponchel, Francesca Greselin, Brendan Murphy

Risultato della ricerca: Contributo in libroContributo a convegno

Abstract

We investigate here the stability of the obtained results of a variable selection method recently introduced in the literature, and embedded into a modelbased 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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteCLADAG 2021
Pagine49-52
Numero di pagine4
Volume128
Stato di pubblicazionePubblicato - 2021
EventoScientific Meeting Classification and Data Analysis Group - Firenze
Durata: 9 set 202111 set 2021

Convegno

ConvegnoScientific Meeting Classification and Data Analysis Group
CittàFirenze
Periodo9/9/2111/9/21

Keywords

  • Variable selection
  • Robust classification
  • Label noise
  • Agri-food
  • Near infrared spectroscopy
  • Mid infrared spectroscopy
  • Outlier detection

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