Validation of two discriminant strategies applied to NIRS data spectra for detection of animal meals in feedstuffs

  1. Soldado Cabezuelo, Ana Belén
  2. Quevedo Pérez, José Ramón
  3. Bahamonde Rionda, Antonio
  4. Modroño Lozano, S.
  5. Martínez Fernández, Adela
  6. Vicente Mainar, Fernando
  7. Pérez Marín, Dolores
  8. Garrido Varo, Ana
  9. Guerrero Ginel, José Emilio
  10. Roza Delgado, María Begoña de la
Revista:
Spanish journal of agricultural research

ISSN: 1695-971X,  2171-9292

Año de publicación: 2011

Volumen: 9

Número: 1

Páginas: 41-48

Tipo: Artículo

DOI: 10.5424/SJAR/20110901-138-10 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Spanish journal of agricultural research

Objetivos de desarrollo sostenible

Resumen

For developing qualitative or quantitative applications with spectroscopic data, such as near infrared spectroscopy (NIRS), different methodologies have been proposed in the mathematical statistical and computer science literature. Useful chemometrical alternatives have emerged, such as support vector machines (SVM), widely used for modeling multivariate and non-linear systems. These methods are usually compared using the classification performance and the success of results. The aim of the present work was to develop and validate a robust, accurate and fast discriminant methodology based on NIRS data to detect presence of animal meals in feedstuffs. A linear method, modified partial least square (PLS) analysis and one non-linear method (SVM) were studied. Results showed that modified PLS model allows obtaining coefficients of determination for cross validation around 0.97. Applying SVM strategy no false negatives were detected during training step. With both strategies the lowest percentage of misclassified samples on external validation was achieved with SVM, 0% with certified standard samples containing from 0.05% to 4% of animal meals. These results show SVM strategy as a robust method of classification for detecting animal meals in feedstuffs using NIRS methodology.

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