Literature DB >> 28372157

Discrimination of whisky brands and counterfeit identification by UV-Vis spectroscopy and multivariate data analysis.

Angélica Rocha Martins1, Márcio Talhavini2, Maurício Leite Vieira2, Jorge Jardim Zacca2, Jez Willian Batista Braga3.   

Abstract

The discrimination of whisky brands and counterfeit identification were performed by UV-Vis spectroscopy combined with partial least squares for discriminant analysis (PLS-DA). In the proposed method all spectra were obtained with no sample preparation. The discrimination models were built with the employment of seven whisky brands: Red Label, Black Label, White Horse, Chivas Regal (12years), Ballantine's Finest, Old Parr and Natu Nobilis. The method was validated with an independent test set of authentic samples belonging to the seven selected brands and another eleven brands not included in the training samples. Furthermore, seventy-three counterfeit samples were also used to validate the method. Results showed correct classification rates for genuine and false samples over 98.6% and 93.1%, respectively, indicating that the method can be helpful for the forensic analysis of whisky samples.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Chemometrics; Falsification; Forensic; UV–Vis; Whisky

Mesh:

Year:  2017        PMID: 28372157     DOI: 10.1016/j.foodchem.2017.02.024

Source DB:  PubMed          Journal:  Food Chem        ISSN: 0308-8146            Impact factor:   7.514


  2 in total

1.  Photoluminescent Recognition of Strong Alcoholic Beverages with Carbon Nanoparticles.

Authors:  Ivan I Ivanov; Alexander N Zaderko; Vladimir Lysenko; Thierry Clopeau; Vladyslav V Lisnyak; Valeriy A Skryshevsky
Journal:  ACS Omega       Date:  2021-07-12

2.  Bare carbon electrodes as simple and efficient sensors for the quantification of caffeine in commercial beverages.

Authors:  Luca Redivo; Miroslav Stredanský; Elisabetta De Angelis; Luciano Navarini; Marina Resmini; Ĺubomír Švorc
Journal:  R Soc Open Sci       Date:  2018-05-02       Impact factor: 2.963

  2 in total

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