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Microwave–vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks

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Abstract

Drying characteristics of sour cherries were determined using microwave vacuum drier at various microwave powers (360, 600, 840, 1200 W) and absolute pressures (200, 400, 600, 800 mbars). In addition, using the artificial neural networks (ANN), trained by standard Back-Propagation algorithm, the effects of microwave power, pressure and drying time on moisture ratio (MR) and drying rate (DR) were investigated Based on the evaluation of experimental data fitting with semi-theoretical and empirical models, the Midilli et al. model was selected as the most appropriate one. Furthermore, the ANN model was able to predict the moisture ratio and drying rate quite well with determination coefficients (R2) of 0.9996, 0.9961 and 0.9958 for training, validation and testing, respectively. The prediction Mean Square Error of ANN was about 0.0003, 0.0071 and 0.0053 for training, validation and testing, respectively. This parameter signifies the difference between the desired outputs (as measured values) and the simulated values by the model. The good agreement between the experimental data and ANN model leads to the conclusion that the model adequately describes the drying behavior of sour cherries, in the range of operating conditions tested.

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Authors and Affiliations

  1. Department of Agricultural Machinery Engineering, Faculty of Agriculture, Tarbiat Modares University, P. O Box 14115-336, Tehran, Iran

    Ali Motavali, Gholam Hassan Najafi & Saeid Minaei

  2. Department of Food Science and Technology, Faculty of Agriculture, Tarbiat Modares University, P. O Box 14115-336, Tehran, Iran

    Solayman Abbasi & Abdurrahman Ghaderi

Authors
  1. Ali Motavali
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  2. Gholam Hassan Najafi
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  3. Solayman Abbasi
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  4. Saeid Minaei
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  5. Abdurrahman Ghaderi
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Correspondence to Ali Motavali.

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Motavali, A., Najafi, G.H., Abbasi, S. et al. Microwave–vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks. J Food Sci Technol 50, 714–722 (2013). https://doi.org/10.1007/s13197-011-0393-1

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  • DOI: https://doi.org/10.1007/s13197-011-0393-1

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