Literature DB >> 29574347

Authenticity assessment of banknotes using portable near infrared spectrometer and chemometrics.

Vanessa da Silva Oliveira1, Ricardo Saldanha Honorato2, Fernanda Araújo Honorato3, Claudete Fernandes Pereira4.   

Abstract

Spectra recorded using a portable near infrared (NIR) spectrometer, Soft Independent Modeling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) associated to Successive Projections Algorithm (SPA) models were applied to identify counterfeit and authentic Brazilian Real (R$20, R$50 and R$100) banknotes, enabling a simple field analysis. NIR spectra (950-1650nm) were recorded from seven different areas of the banknotes (two with fluorescent ink, one over watermark, three with intaglio printing process and one over the serial numbers with typography printing). SIMCA and SPA-LDA models were built using 1st derivative preprocessed spectral data from one of the intaglio areas. For the SIMCA models, all authentic (300) banknotes were correctly classified and the counterfeits (227) were not classified. For the two classes SPA-LDA models (authentic and counterfeit currencies), all the test samples were correctly classified into their respective class. The number of selected variables by SPA varied from two to nineteen for R$20, R$50 and R$100 currencies. These results show that the use of the portable near-infrared with SIMCA or SPA-LDA models can be a completely effective, fast, and non-destructive way to identify authenticity of banknotes as well as permitting field analysis.
Copyright © 2018 Elsevier B.V. All rights reserved.

Keywords:  Banknotes; Chemometrics; Counterfeit; Multivariate classification; Portable near-infrared instrument

Year:  2018        PMID: 29574347     DOI: 10.1016/j.forsciint.2018.03.001

Source DB:  PubMed          Journal:  Forensic Sci Int        ISSN: 0379-0738            Impact factor:   2.395


  1 in total

1.  Counterfeit fifty Ringgit Malaysian banknotes authentication using novel graph-based chemometrics method.

Authors:  Nurfarhana Hassan; Tahir Ahmad; Naji Arafat Mahat; Hasmerya Maarof; Foo Keat How
Journal:  Sci Rep       Date:  2022-03-22       Impact factor: 4.379

  1 in total

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