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Modélisation ANN des conditions d'entreposage optimales basée sur les caractéristiques viscoélastiques et [l']évaluation sensorielle du riz cuit congelé.

ANN modeling for optimum storage condition based on viscoelastic characteristics and sensory evaluation of frozen cooked rice.

Auteurs : KONO S., KAWAMURA I., ARAKI T., et al.

Type d'article : Article, Article de la RIF

Résumé

The optimum frozen condition of cooked rice has been predicted by artificial neural network (ANN) based on the data obtained from sensory evaluations as well as viscoelastic measurements. Cooked rice was frozen and stored at -5, -15, and -45?°C for 0, 10, 30, 60 and 90 days. Then, after the samples were thawed by natural convection air at room temperature or microwave heating, the viscoelastic parameters were measured with the Tensipresser and sensory scores were evaluated by a 7-point scale. The sensory scores were predicted with high accuracy from the viscoelastic parameters by ANN models. In addition, the ANN model analysis using the dataset of storage conditions and palatability scores showed that to achieve palatability score greater than the central value of 4.0 after 40 days, storage temperature must be below -25?°C if air thawing by natural convection is used and below -15?°C if microwave thawing and heating are used.

Documents disponibles

Format PDF

Pages : 218-227

Disponible

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    20 €

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    Gratuit

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Détails

  • Titre original : ANN modeling for optimum storage condition based on viscoelastic characteristics and sensory evaluation of frozen cooked rice.
  • Identifiant de la fiche : 30017516
  • Langues : Anglais
  • Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 65
  • Date d'édition : 05/2016

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