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Multivariate Bayesian cognitive modeling for unsupervised quality control of baked pizzas


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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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).

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  • Bayesian modeling
  • Multivariate statistics
  • Geometric color space locus