title: Wine authenticity verification as a forensic problem: an application of likelihood ratio test to label verification.
abstract: 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.
authors.affiliation: Faculty of Chemistry, Jagiellonian University, Ingardena 3, 30-060 Krakow, Poland. Electronic address: rzepecka@chemia.uj.edu.pl.
authors.forename: Agnieszka
authors.initials: A
authors.lastname: Martyna
authors.affiliation: Institute of Forensic Research, Westerplatte 9, 31-033 Krakow, Poland; Department of Analytical Chemistry, Chemometric Research Group, Institute of Chemistry, The University of Silesia, Szkolna 9, 40-006 Katowice, Poland. Electronic address: gzadora@ies.krakow.pl.
authors.forename: Grzegorz
authors.initials: G
authors.lastname: Zadora
authors.affiliation: Department of Analytical Chemistry, Chemometric Research Group, Institute of Chemistry, The University of Silesia, Szkolna 9, 40-006 Katowice, Poland. Electronic address: ivana.stanimirova-daszykowska@us.edu.pl.
authors.forename: Ivana
authors.initials: I
authors.lastname: Stanimirova
authors.affiliation: Institute of Forensic Science and Security and ATVS - Biometric Recognition Group (ATVS), Universidad Autonoma de Madrid, C/Francisco Tomas y Valiente 11, 28049 Madrid, Spain. Electronic address: daniel.ramos@uam.es.
authors.forename: Daniel
authors.initials: D
authors.lastname: Ramos
mesh_terms: D016002:Discriminant Analysis; D005506:Food Contamination / Q000032:analysis*; D005515:Food Labeling; D016013:Likelihood Functions; D014920:Wine / Q000032:analysis* / Q000145:classification / Q000706:statistics & numerical data
keywords: Classification problem; Empirical cross entropy; Evaluation of forensic evidence; Food products authenticity; Likelihood ratio