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.
Detecting wine adulterations employing robust mixture of factor analyzers / A. Cappozzo, F. Greselin (STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION). - In: Statistical Learning of Complex Data / [a cura di] F. Greselin, L. Deldossi, L. Bagnato, M. Vichi. - [s.l] : Springer, 2019. - ISBN 978-3-030-21139-4. - pp. 13-21 (( Intervento presentato al 11. convegno Biannual meeting of the Classification and Data Analysis Group tenutosi a Milano nel 2017 [10.1007/978-3-030-21140-0_2].
Detecting wine adulterations employing robust mixture of factor analyzers
A. Cappozzo;
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.File | Dimensione | Formato | |
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