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Multivariate Bayesian cognitive modeling for unsupervised quality control of baked pizzas

Machine Vision and Applications volume 23, pages 491–499 (2012)Cite this article

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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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

Keywords

  • Bayesian modeling
  • Multivariate statistics
  • Geometric color space locus