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

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

Department of Physics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 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 language	English
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

Conference

Conference	20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012
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

Cite this

Backhaus, A., Ashok, P. C., Praveen, B. B., Dholakia, K., & Seiffert, U. (2012). Classifying scotch whisky from near-infrared Raman spectra with a radial basis function network with relevance learning. In ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 411-416). (ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication.

Backhaus, Andreas ; Ashok, Praveen C. ; Praveen, Bavishna B. ; Dholakia, Kishan ; Seiffert, Udo. / Classifying scotch whisky from near-infrared Raman spectra with a radial basis function network with relevance learning. ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2012. pp. 411-416 (ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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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.",

author = "Andreas Backhaus and Ashok, {Praveen C.} and Praveen, {Bavishna B.} and Kishan Dholakia and Udo Seiffert",

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series = "ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

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Backhaus, A, Ashok, PC, Praveen, BB, Dholakia, K & Seiffert, U 2012, Classifying scotch whisky from near-infrared Raman spectra with a radial basis function network with relevance learning. in ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, i6doc.com publication, pp. 411-416, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012, Bruges, Belgium, 12/4/25.

Classifying scotch whisky from near-infrared Raman spectra with a radial basis function network with relevance learning. / Backhaus, Andreas; Ashok, Praveen C.; Praveen, Bavishna B.; Dholakia, Kishan; Seiffert, Udo.

ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2012. p. 411-416 (ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Seiffert, Udo

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Backhaus A, Ashok PC, Praveen BB, Dholakia K, Seiffert U. Classifying scotch whisky from near-infrared Raman spectra with a radial basis function network with relevance learning. In ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication. 2012. p. 411-416. (ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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

Abstract

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Bibliographical note

All Science Journal Classification (ASJC) codes

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