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Multivariate Bayesian cognitive modeling for unsupervised quality control of baked pizzas

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Abstract

The present article describes a Bayesian multivariate methodology developed for unsupervised quality control of pizzas based on RGB color attributes. A sensory experiment was done to define the readiness point ground truth. During the validation phase, different pizza samples were baked at a different temperature. The cheese and crust color patterns were statistically compared against the ground truth to check the readiness point. Results show that the proposed methodology presents a good performance demonstrating that color attributes can be used as an unsupervised quality control using traditional statistical methods.

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References

  1. Abdullah M.Z.: Quality evaluation of bakery products. In: Sun, D.W. (eds) Computer Vision Technology for Food Quality Evaluation, pp. 481–522. Academic Press, Amsterdam (2008). doi:10.1016/B978-012373642-0.50023-5

    Chapter  Google Scholar 

  2. de Aguiar, D.B.: Metodologia bayesiana para o controle de qualidade de pizzas (bayesian methodology for quality control of pizzas - undergraduate project-ufsc-ine-informatic and statistics department). Tech. rep., Federal University of Santa Catarina, Brazil (2008)

  3. Chater, N., Tenenbaum, J.B., Yuille, A.: Probabilistic models of cognition: Conceptual foundations. Trends Cognit. Sci. 10(7): 287–291 (2006). doi:10.1016/j.tics.2006.05.007 (special issue: probabilistic models of cognition)

    Google Scholar 

  4. Du C.J., Sun D.W.: Recent developments in the applications of image processing techniques for food quality evaluation. Trends Food Sci. Technol. 15(5), 230–249 (2004). doi:10.1016/j.tifs.2003.10.006

    Article  Google Scholar 

  5. Du C.J., Sun D.W.: Comparison of three methods for classification of pizza topping using different colour space transformations. J. Food Eng. 68(3), 277–287 (2005). doi:10.1016/j.jfoodeng.2004.05.044

    Article  Google Scholar 

  6. Du C.J., Sun D.W.: Pizza sauce spread classification using colour vision and support vector machines. J. Food Eng. 66(2), 137–145 (2005). doi:10.1016/j.jfoodeng.2004.03.011

    Article  Google Scholar 

  7. Du C.J., Sun D.W.: Learning techniques used in computer vision for food quality evaluation: a review. J. Food Eng. 72(1), 39–55 (2006). doi:10.1016/j.jfoodeng.2004.11.017

    Article  Google Scholar 

  8. Duda R.O., Hart P.E., Stork D.G.: Patterns Classification, 2nd edn. Wiley, New York (2001)

    Google Scholar 

  9. Gunasekaran S.: Computer vision technology for food quality assurance. Trends Food Sci. Technol. 7(8), 245–256 (1996). doi:10.1016/0924-2244(96)10028-5

    Article  Google Scholar 

  10. Jain A., Duin R., Mao J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22, 4 (2000)

    Article  Google Scholar 

  11. Jiang X., Irniger C., Bunke H.: Distance measures for image segmentation evaluation. EURASIP J. Appl. Signal Process. 2006, 1–10 (2006)

    MATH  Google Scholar 

  12. Johnson R.A., Wichern D.W.: Multivariate Statistical Analysis. Prentice Hall, New Jersey (1998)

    Google Scholar 

  13. Mahalanobis P.C.: On the Generalized Distance in Statistics, pp. 49–55. National Institute of Science, India (1936)

    Google Scholar 

  14. Montgomery D.C.: Introduction to Statistical Quality Control. Wiley, New York (2001)

    Google Scholar 

  15. Montgomery D.C.: Design and Analysis of Experiments. Wiley, New York (2005)

    MATH  Google Scholar 

  16. NIST: Engineering statistics handbook. Tech. rep., NIST-National Institute Of Standards (2006)

  17. Purlis E., Salvadori V.O.: Bread browning kinetics during baking. J. Food Eng. 80(4), 1107–1115 (2007). doi:10.1016/j.jfoodeng.2006.09.007

    Article  Google Scholar 

  18. Russel S., Norvig P.: Artificial Inteligence. Elsevier, Amsterdam (2004)

    Google Scholar 

  19. Santos, B.S.: Determinação das condições térmicas de cocção e das propriedades termo-físicas da pizza (determination of thermal cooking and thermophysics properties of pizza). Master’s thesis, UFSC-Universidade Federal de Santa Catarina (2009)

  20. Sommier A., Chiron H., Colonna P., Valle G.D., Rouill J.: An instrumented pilot scale oven for the study of french bread baking. J. Food Eng. 69(1), 97–106 (2005). doi:10.1016/j.jfoodeng.2004.07.015

    Article  Google Scholar 

  21. Sun D.W., Brosnan T.: Pizza quality evaluation using computer vision–part 1: Pizza base and sauce spread. J. Food Eng. 57(1), 81–89 (2003). doi:10.1016/S0260-8774(02)00275-3

    Article  Google Scholar 

  22. Sun D.W., Brosnan T.: Pizza quality evaluation using computer vision–part 2: pizza topping analysis. J. Food Eng. 57(1), 91–95 (2003). doi:10.1016/S0260-8774(02)00276-5

    Article  Google Scholar 

  23. Tenenbaum, J.B., Griffiths, T.L., Kemp, C.: Theory-based bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences 10(7):309–318 (2006). doi:10.1016/j.tics.2006.05.009, (special issue: probabilistic models of cognition)

    Google Scholar 

  24. Wang H.H., Sun D.W.: Assessment of cheese browning affected by baking conditions using computer vision. J. Food Eng. 56(4), 339–345 (2003). doi:10.1016/S0260-8774(02)00159-0

    Article  Google Scholar 

  25. Yam, K.L., Papadakis, S.E.: A simple digital imaging method for measuring and analyzing color of food surfaces. J. Food Eng. 61(1):137–142 (2004). doi:10.1016/S0260-8774(03)00195-X (applications of computer vision in the food industry)

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Authors and Affiliations

  1. INPE-CCST and UFSC-EGC/INCOD/LEPTEN, Campus Trindade, Florianopolis, SC, 88040-900, Brazil

    Sylvio Luiz Mantelli Neto

  2. UFSC-INE-INCOD/LEPTEN, Campus Trindade, Florianopolis, SC, 88040-900, Brazil

    Daniel Besen de Aguiar

  3. UFSC-EMC-LEPTEN, Campus Trindade, Florianopolis, SC, 88040-900, Brazil

    Bianca Sens dos Santos

  4. UFSC-INE-INCOD/EGC, Campus Trindade, Florianopolis, SC, 88040-900, Brazil

    Aldo von Wangenheim

Authors
  1. Sylvio Luiz Mantelli Neto
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  2. Daniel Besen de Aguiar
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  3. Bianca Sens dos Santos
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  4. Aldo von Wangenheim
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Correspondence to Sylvio Luiz Mantelli Neto.

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Mantelli Neto, S.L., de Aguiar, D.B., dos Santos, B.S. et al. Multivariate Bayesian cognitive modeling for unsupervised quality control of baked pizzas. Machine Vision and Applications 23, 491–499 (2012). https://doi.org/10.1007/s00138-011-0339-7

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  • DOI: https://doi.org/10.1007/s00138-011-0339-7

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