Advertisement

Assessing the Quality Level of Corn Tortillas with Inductive Characterization and Digital Image Analysis

  • Marco A. Moreno-Armendáriz
  • Salvador Godoy-Calderon
  • Hiram Calvo
  • Oscar M. Rojas-Padilla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)

Abstract

Characterization and classification of corn tortillas turns out to be an extremely delicate and difficult process when dealing with regulations for import/export and production process certification.  In this paper we present a method for non-invasive feature extraction, based on digital imaging and a series of procedures to characterize different qualities of corn tortillas for their later classification.  The novelty in this whole method lies in the extremely reduced set of features required for the characterization with only geometrical and color features.  Nonetheless, this set of features can assess diverse quality elements like the homogeneity of the baking process and others alike.  Experimental results on a sample batch of 600 tortillas show the presented method to be around 95% effective.

Keywords

Inductive characterization digital image analysis corn tortilla 

References

  1. 1.
    Adams, J., Parulski, K., Spaulding, K.: Color processing in digital cameras. IEEE Micro. 18(6), 20–30 (1998)CrossRefGoogle Scholar
  2. 2.
    Cai, W., Yu, Q., Wang, H.: A fast contour-based approach to circle and ellipse detection. In: Fifth World Congress on Intelligent Control and Automation, vol. 5, pp. 4686–4690 (2004)Google Scholar
  3. 3.
    Clark, P., Tim, N.: The CN2 induction algorithm, Tahoe City,CA (1989)Google Scholar
  4. 4.
    Cortés-Goméz, A., Martín-Martínez, E.S., Martínez-Bustos, F., Vázquez-Carrillo, G.M.: Tortillas of Blue Maize (zeamays l.) Prepared by a Fractioned Process of Nixtamalization: analisys using response surface methodology. J. of Food Eng. 60(3), 273–281 (2001)Google Scholar
  5. 5.
    Domingos, P.: Unifying Instance-Based and Rule-Based Induction. Machine Learning 24(2) (1996)Google Scholar
  6. 6.
    Gonzalez, R., Woods, R.: Digital Image Processing, 3rd edn. Prentice Hall, Pearson (2008)Google Scholar
  7. 7.
    Gupta, L., Srinath, M.D.: Contour Sequence Moments for the Classification of Closed Planar Shapes. Pattern Recognition 20(3), 267–272 (1987)CrossRefGoogle Scholar
  8. 8.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer (2003)Google Scholar
  9. 9.
    Herrera-Corredor, J., Saidu, J., Khachatryan, A., Prinyawiwatkul, W., Carballo-Carballo, A., Zepeda-Bautista, R.: Identifying Drivers for Consumers Acceptance and Purchase Intent of Corn Tortilla. J. of Food Sc. 72(9), 727–730 (2007)CrossRefGoogle Scholar
  10. 10.
    Ibarra-Manzano, M.A., Devy, M., Boizard, J.L.: Real-time classification based on color and texture attributes on an FPGA-based architecture. In: Conference on Design and Architectures for Signal and Image Processing, pp. 250–257 (2010)Google Scholar
  11. 11.
    Malamas, E.N., Petrakis, E.G.M., Zervakis, M., Petit, L., Legat, J.D.: A Survey on Industrial Vision Systems, Aplications and Tools. Image and Vision Computing 21(3), 171–188 (2003)CrossRefGoogle Scholar
  12. 12.
    Mery, D., Chanona-Pérez, J.J., Soto, A., Aguilera, J.M., Vélez-Rivera, N., Arzate-Vázquez, I., Gutiérrez-López, G.F.: Quality Classification of Corn Tortillas using Computer Vision. J. of Food Eng. 101, 357–364 (2010)CrossRefGoogle Scholar
  13. 13.
    Michalski, R.S., Stepp, R.: Automated Construction of Classifications: Conceptual Clustering versus Numerical Taxonomy. IEEE Trans. on Pattern Analysis and Machine Intelligence PAMI-5(4), 396–410 (1983)CrossRefGoogle Scholar
  14. 14.
    Michalski, R.S.: A theory and methodology of inductive learning  20 (1983)Google Scholar
  15. 15.
    Michalski, R.S., Chilausky, R.L.: Learning by being told and learning form examples: an experimental comparison of the two methods of knowledge adquisition in the context of developing an extra system for sorbeandesease diagnosis. Policy Analysis and Information Systems 4(2), 125–160 (1980)Google Scholar
  16. 16.
    Michalski, R.S., Mozetic, I., Hong, J., Lavrac, N.: The multi-purpose incremental learning system AQ15 and its testing application to three medical domains. In: Proceedings of the Fifth National Conference on Artificial Intelligence, Philadelph (1986)Google Scholar
  17. 17.
    Norma-Oficial-Mexicana, NOM-187-SSA1/SCFI-2002: Productos y servicios. Masa, tortillas, tostadas y harinas preparadas para su elaboración y establecimientos donde se procesan. Especificaciones sanitarias. Secretaría de Economía, Estados Unidos Mexicanos (2003) Google Scholar
  18. 18.
    Rivest, R.L.: Learning decision lists. Machine Learning, 229–246 (1987)Google Scholar
  19. 19.
    Soille, Morphological Image Analisys. Springer (2004) Google Scholar
  20. 20.
    Tahir, M.A., Bouridane, A.: An Fpga Based Coprocessor for Cancer Classification Using Nearest Neighbour Classifier. In: IEEE International Conference on  Acoustics, Speech and Signal Processing, vol. 3, pp. III-1012—III-1015 (2006)Google Scholar
  21. 21.
  22. 22.
    Watanabe, T., Matsumoto, M.: Recognition of Circle Form Using Fuzzy Sequential System. In: Twenty-First International Symposium on Multiple-Valued Logic, pp. 85–92 (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marco A. Moreno-Armendáriz
    • 1
  • Salvador Godoy-Calderon
    • 1
  • Hiram Calvo
    • 1
  • Oscar M. Rojas-Padilla
    • 1
  1. 1.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMéxico, D. F.México

Personalised recommendations