Improving the Discriminatory Power of a Near-Infrared Microscopy Spectral Library with a Support Vector Machine Classifier

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Abstract:

A multi-group classifier based on the support vector machine (SVM) has been developed for use with a library of 48 456 spectra measured by near-infrared reflection microscopy (NIRM) on 227 samples representing 26 animal feed ingredients and 4 possible contaminants of animal origin. The performance of the classifier was assessed by a five-fold cross-validation, dividing at the sample level. Although the overall proportion of misclassifications was 27%, almost all of these involved the confusion of pairs of similar ingredients of vegetable origin. Such confusions are unimportant in the context of the intended use of the library, which is the detection of banned ingredients in animal feed. The error rate in discrimination between permitted and banned ingredients was just 0.17%. The performance of the SVM classifier was substantially better than that of the K-nearest-neighbors method employed in previous work with the same library, for which the comparable error rates are 36% overall and 0.39% for permitted versus banned ingredients.

Keywords: ANIMAL FEEDS; CLASSIFICATION; INGREDIENTS; NEAR-INFRARED MICROSCOPY; NIR REFLECTION MICROSCOPY; SPECTRAL LIBRARIES; SUPPORT VECTOR MACHINE

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/000370210790572124

Affiliations: 1: Department of Animal Nutrition, Grasslands and Forages, Regional Institute for Research and Agro-Food Development, SERIDA, PO Box 13, 33300 Villaviciosa, Spain 2: Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK 3: Department of Computer Science, University of Oviedo, Campus de Viesques, Gijón 33204, Spain 4: Artificial Intelligence Center, University of Oviedo, Campus de Viesques, Gijón 33204, Spain

Publication date: January 1, 2010

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