Improving the discriminatory power of a near-infrared microscopy spectral library with a support vector machine classifier

Applied Spectroscopy

Volumn 64, Issue 1, 2010, Pages 66-72

  Fernandez IBANEZ V ^a   Fearn, T ^b   Montanes E ^c   Quevedo, J R ^c   Soldado, A ^a   De La Roza Delgado, B ^a  

^a Área de Cultivos Hortofrutícolas y Forestales   (Spain)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY OF OVIEDO   (Spain)

Author keywords

Animal feeds; Classification; Ingredients; Near infrared microscopy; NIR reflection microscopy; Spectral libraries; Support vector machine

Indexed keywords

ANIMALS; CLASSIFICATION (OF INFORMATION); DISCRIMINATORS; INFRARED DEVICES; NEAREST NEIGHBOR SEARCH;

ANIMAL FEED; DISCRIMINATORY POWER; INGREDIENTS; K-NEAREST NEIGHBORS METHOD; MISCLASSIFICATIONS; NEAR-INFRARED MICROSCOPIES; SPECTRAL LIBRARIES; SUPPORT VECTOR MACHINE CLASSIFIERS;

SUPPORT VECTOR MACHINES;

EID: 77649220631     PISSN: 00037028     EISSN: None     Source Type: Journal    
DOI: 10.1366/000370210790572124     Document Type: Article

References (30)

1. European Union. Regulation (EC) No. 178/2002. Off. J. Eur. Comm. L 31, 1-24 (2002).
2. European Union. Regulation (EC) No. 152/2009. Off. J. Eur. Comm. L 54, 1-130 (2009).
3. D. Pérez-Marín, A. Garrido-Varo, J. E. Guerrero-Ginel, and A. GómezCabrera, Animal Feed Sci. Technol. 116, 333 (2004).
4. D. Pérez-Marín, A. Garrido-Varo, and J. E. Guerrero, Appl. Spectrosc. 60, 1432 (2006).
5. L. W. D. van Raamsdonk, C. von Hoist, V. Baeten, G. Berben, A. Boix, and J. de Jong, Animal Feed Sci. Technol. 133, 73 (2007).
6. B. de la Roza-Delgado, A. Soldado, A. Martinez-Fernandez, F. Vicente, A. Garrido-Varo, D. Pérez-Marín, M. J. de la Haba, and J. E. Guerrero-Ginel, Food Chem. 105, 1164 (2007).
7. M. J. De la Haba, J. A. Fernández-Pierna, O. Furniere, A. Garrido-Varo, J. E. Guerrero, D. C. Pérez-Marín, P. Dardenne, and V. Baeten, J. Near Infrared Spectrosc. 15, 81 (2007).
8. V. Baeten, C. von Holst, A. Garrido, J. Vancutsem, A. M. Renier, and P. Dardenne, Anal. Bioanal. Chem. 382, 149 (2005).
9. C. von Hoist, V. Baeten, A. Boix, B. Slowikowski, J. A. Fernández-Pierna, S. Tirendi, and P. Dardenne, Anal. Bioanal. Chem. 392, 313 (2008).
10. V. Femández-Ibáñez, T. Fearn, A. Soldado, and B. de la Roza-Delgado, "Development and validation of near infrared microscopy spectral libraries of ingredients in animal feed as a first step to adopting traceability and authenticity as guarantors of food safety", Food Chem., paper submitted (2009).
11. M. V. Fernández-Ibáñez, A. Soldado, F. Vicente, A. Martínez-Fernández, and B. de la Roza-Delgado, J. Near Infrared Spectrosc. 16, 43 (2008).
12. F. Piraux and P. Dardenne, "Feed authentication by near-infrared microscopy", in Near Infrared Spectroscopy: Proceedings of the 9th International Conference, A. M. C. Davies and R. Giangiacomo, Eds. (NIR Publications, Chichester, UK, 2000), p. 535.
13. V. Baeten, A. Michotte Renier, G. Sinnaeve, and P. Dardenne, "Analyses of feedingstuffs by near-infrared microscopy (NIRM): detection and quantification of meat and bone meal (MBM)", in Proceedings of the 6th International Symposium on Food Authenticity and Safety (Nantes, 2001), p. 1.
14. T. Naes, T. Isaksson, T. Feam, and T. Davies, Multivariate Calibration and Classification (NTR Publications, Chichester, UK, 2002), p. 221.
15. T. Joachims, Making Large-Scale Support Vector Machine Learning Practical Advances in Kernel Methods: Support Vector Machines, B. Scholkopf, C. Burges, and A. Smola, Eds. (MIT Press, Cambridge, MA, 1998).
16. D. Perez-Marin, A. Garrido-Varo, J. E. Guerrero, T. Feam, and A. M. C. Davies, Appl. Spectrosc. 62, 536 (2008).
17. R. P. Cogdill and P. Dardenne, J. Near Infrared Spectrosc. 12, 93 (2004).
18. J. R. Quinlan, C45. Programs for Machine Learning, M. Kaufmann, Ed. (Morgan Kauffman Publishers, San Francisco, 1993).
19. IR Spectroscopy Software: User's Guide (Perkin-Elmer Instruments LLC, UK, 2002).
20. S. Lukaszyk, Comput. Mech. 33, 299 (2004).
21. V. N. Vapnik, The Nature of Statistical Approaches to Text Categorization (Springer-Verlag, New York, 1995).
22. B. Scholkopf, C. Burges, and V. Vapnik, "Extracting Support Data for a Given Task", in 1st International Conference on Knowledge Discovery and Data Mining (Menlo Park, Canada, 1995).
23. O. Chapelle, P. Haffner, and V. N. Vapnik, Support Vector Machines for Histogram-Based Image Classification IEEE Transactions on Neural Networks, 10(5), (1999).
24. J. Fiirnkranz, J. Machine Learning Res. 2, 721 (2002).
25. V. Koltchinskii, D. Panchenko, and F. Lozano, "Further explanation of the effectiveness of voting methods: The game between margins and weights", in Proceedings 14th Annual Conference on Computational Learning Theory and 5th European Conference on Computational Learning Theory (Amsterdam, The Netherlands, 2004), p. 241.
26. J. Fiirnkranz, "Round Robin Rule Learning", in Proceedings of the 18th International Conference on Machine Learning (ICML-01) (Warsaw, Poland, 2001), p. 146.
27. C.-C. Chang and C-J. Lin, LIBSVM: A Library for Support Vector Machines (2001), software available at http://www.csie.ntu.edu.tw/-cjlin/libsvm.
28. J. Weston, A. Elisseeff, G. Bakir, and F. Sinz,
29. T. Dietterich, Neural Comput. 10, 1895 (1998).
30. F. Sebastiani, ACM Computing Survey 34(1), (2002).