Wine authenticity verification as a forensic problem: An application of likelihood ratio test to label verification
Entidad
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesEditor
Elsevier BVFecha de edición
2014-05-01Cita
10.1016/j.foodchem.2013.10.111
Food Chemistry 150 (2014): 287 – 295
ISSN
0308-8146 (print); 1873-7072 (online)DOI
10.1016/j.foodchem.2013.10.111Versión del editor
http://dx.doi.org/doi:10.1016/j.foodchem.2013.10.111Materias
Classification problem; Empirical cross entropy; Evaluation of forensic evidence; Food products authenticity; Likelihood ratio; Informática; TelecomunicacionesNota
This is the author’s version of a work that was accepted for publication in Food Chemistry. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Food Chemistry, 150, (2014) DOI: 10.1016/j.foodchem.2013.10.111Derechos
© 2014 Elsevier B.V. All rights reservedEsta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Resumen
The aim of the study was to investigate the applicability of the likelihood ratio (LR) approach for verifying the authenticity of 178 samples of 3 Italian wine brands: Barolo, Barbera, and Grignolino described by 27 parameters describing their chemical compositions. Since the problem of products authenticity may be of forensic interest, the likelihood ratio approach, expressing the role of the forensic expert, was proposed for determining the true origin of wines. It allows us to analyse the evidence in the context of two hypotheses, that the object belongs to one or another wine brand. Various LR models were the subject of the research and their accuracy was evaluated by the Empirical cross entropy (ECE) approach. The rates of correct classifications for the proposed models were higher than 90% and their performance evaluated by ECE was satisfactory.
Lista de ficheros
Google Scholar:Martyna, Agnieszka
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Zadora, Grzegorz
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Stanimirova, Ivana
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Ramos Castro, Daniel
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