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Utilice este identificador para citar o enlazar este documento: https://ria.asturias.es/RIA/handle/123456789/11849


Título: Improving the Discriminatory Power of a Near-Infrared Microscopy Spectral Library with a Support Vector Machine Classifier
Autores: Fernández Ibáñez, V.
Fearn, T.
Montañés, E.
Quevedo, J.
Soldado, A.
Roza Delgado, B., de la
Palabras Claves: Microscopía de infrarrojo cercano
Microscopía de reflexión NIR
Máquinas de vectores soporte
Alimentos para animales
Fecha Edición: 2010
Editor: SAGE Publications
Cita Bibliográfica: Fernández Ibáñez, V... [et al.]. Improving the Discriminatory Power of a Near-Infrared Microscopy Spectral Library with a Support Vector Machine Classifier. Applied Spectroscopy. 2010 ; 64 : 66-72
Resumen: 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 nearinfrared 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.
URI: https://ria.asturias.es/RIA/handle/123456789/11849
ISSN: 0003-7028
Aparece en las Colecciones:Agroalimentación y Ganadería

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