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Effect of moisture content on thermodynamic characteristics of grape: mathematical and artificial neural network modelling

Author

Listed:
  • Reza Amiri Chayjan

    (Department of Agricultural Machinery Engineering and)

  • Mahmood Esna-Ashari

    (Department of Horticultural Sciences, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran)

Abstract

Artificial neural networks (ANNs) and four empirical mathematical models, namely Henderson, GAB, Halsey, and Oswin were used for the estimation of equilibrium moisture content (EMC) of the dried grape (black currant). The results showed that the EMC of the grape were more accurately predicted by ANN models than by the empirical models. The heat and entropy of sorption of the grape have separately been predicted by two mathematical models as a function of EMC with desirable coefficient of determination (R2 ≍ 0.99). At the EMC above 7% (d.b.), the heat and entropy of the grape sorption were smoothly decreased, while they were the highest at the moisture content of about 7% (d.b.). Better equations could be developed for the prediction of the heat of sorption and entropy based on the data from the ANN model.

Suggested Citation

  • Reza Amiri Chayjan & Mahmood Esna-Ashari, 2011. "Effect of moisture content on thermodynamic characteristics of grape: mathematical and artificial neural network modelling," Czech Journal of Food Sciences, Czech Academy of Agricultural Sciences, vol. 29(3), pages 250-259.
    • Handle: RePEc:caa:jnlcjf:v:29:y:2011:i:3:id:328-2009-cjfs
    DOI: 10.17221/328/2009-CJFS
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    Cited by:

    1. Krzysztof Przybył & Piotr Boniecki & Krzysztof Koszela & Łukasz Gierz & Mateusz Łukomski, 2019. "Computer vision and artificial neural network techniques for classification of damage in potatoes during the storage process," Czech Journal of Food Sciences, Czech Academy of Agricultural Sciences, vol. 37(2), pages 135-140.

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