An authentic food is one that is what it claims to be. Nowadays, more and more attention is devoted to the food market: stakeholders, throughout the value chain, need to receive exact information about the specific product they are commercing with. To ascertain varietal genuineness and distinguish potentially doctored food, in this paper we propose to employ a robust mixture estimation method. Particularly, in a wine authenticity framework with unobserved heterogeneity, we jointly perform genuine wine classification and contamination detection. Our methodology models the data as arising from a mixture of Gaussian factors and depicts the observations with the lowest contributions to the overall likelihood as illegal samples. The advantage of using robust estimation on a real wine dataset is shown, in comparison with many other classification approaches. Moreover, the simulation results confirm the effectiveness of our approach in dealing with an adulterated dataset.

Cappozzo, A., Greselin, F. (2019). Detecting wine adulterations employing robust mixture of factor analyzers. In L.D. Francesca Greselin (a cura di), Statistical Learning of Complex Data (pp. 13-21). Springer Berlin Heidelberg [10.1007/978-3-030-21140-0_2].

Detecting wine adulterations employing robust mixture of factor analyzers

Cappozzo, A
;
Greselin, F
2019

Abstract

An authentic food is one that is what it claims to be. Nowadays, more and more attention is devoted to the food market: stakeholders, throughout the value chain, need to receive exact information about the specific product they are commercing with. To ascertain varietal genuineness and distinguish potentially doctored food, in this paper we propose to employ a robust mixture estimation method. Particularly, in a wine authenticity framework with unobserved heterogeneity, we jointly perform genuine wine classification and contamination detection. Our methodology models the data as arising from a mixture of Gaussian factors and depicts the observations with the lowest contributions to the overall likelihood as illegal samples. The advantage of using robust estimation on a real wine dataset is shown, in comparison with many other classification approaches. Moreover, the simulation results confirm the effectiveness of our approach in dealing with an adulterated dataset.
Capitolo o saggio
Food authenticity; Impartial trimming; Mixtures of factor analyzers; Model-based clustering; Robust estimation; Wine adulteration;
Food authenticity; Impartial trimming; Mixtures of factor analyzers; Model-based clustering; Robust estimation; Wine adulteration
English
Statistical Learning of Complex Data
978-3-030-21139-4
Cappozzo, A., Greselin, F. (2019). Detecting wine adulterations employing robust mixture of factor analyzers. In L.D. Francesca Greselin (a cura di), Statistical Learning of Complex Data (pp. 13-21). Springer Berlin Heidelberg [10.1007/978-3-030-21140-0_2].
Cappozzo, A; Greselin, F
File in questo prodotto:
File Dimensione Formato  
Cappozzo-Greselin2019_Chapter_DetectingWineAdulterationsEmpl.pdf

accesso aperto

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 323.35 kB
Formato Adobe PDF
323.35 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/246970
Citazioni
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
Social impact