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

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.