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 language | English |
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Title of host publication | ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
Publisher | i6doc.com publication |
Pages | 411-416 |
Number of pages | 6 |
ISBN (Print) | 9782874190490 |
Publication status | Published - 2012 |
Event | 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012 - Bruges, Belgium Duration: 2012 Apr 25 → 2012 Apr 27 |
Publication series
Name | ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
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Conference
Conference | 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012 |
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Country/Territory | Belgium |
City | Bruges |
Period | 12/4/25 → 12/4/27 |
Bibliographical note
Publisher Copyright:© 2012, i6doc.com publication. All rights reserved.
All Science Journal Classification (ASJC) codes
- Information Systems
- Artificial Intelligence