Classifying scotch whisky from near-infrared Raman spectra with a radial basis function network with relevance learning

Andreas Backhaus, Praveen C. Ashok, Bavishna B. Praveen, Kishan Dholakia, Udo Seiffert

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The instantaneous assessment of high-priced liquor products with minimal sample volume and no special preparation is an important task for quality monitoring and fraud detection. In this contribution the automated classification of Raman spectra acquired with a special optofluidic chip is performed with the use of a number of Artificial Neural Networks. A standard Radial Basis Function Network is adopted to incorporate relevance learning and showed robust classification performance across classification tasks. The acquired relevance weighting per feature dimension can be used to reduce the number of features while retaining a high level of accuracy.

Original languageEnglish
Title of host publicationESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisheri6doc.com publication
Pages411-416
Number of pages6
ISBN (Print)9782874190490
Publication statusPublished - 2012
Event20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012 - Bruges, Belgium
Duration: 2012 Apr 252012 Apr 27

Publication series

NameESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012
CountryBelgium
CityBruges
Period12/4/2512/4/27

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Artificial Intelligence

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