Spray drying of low-phenylalanine skim milk: optimisation of process conditions for improving solubility and particle size

Authors

  • Atefeh Amiri-Rigi,

    1.  Department of Food Science and Technology, Faculty of Nutrition Sciences, Food Science and Technology/National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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  • Zahra Emam-Djomeh,

    1.  Transfer Phenomena Laboratory (TPL), Department of Food Science, Technology and Engineering, Faculty of Agricultural Engineering and Technology, Agricultural Campus of the University of Tehran, PO Box 4111, 31587-11167 Karadj, Iran
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  • Mohammad A. Mohammadifar,

    Corresponding author
    1.  Department of Food Science and Technology, Faculty of Nutrition Sciences, Food Science and Technology/National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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  • Mehrdad Mohammadi

    1.  Department of Food Technology Research, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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E-mail: mohamdif@ut.ac.ir

Summary

The optimisation of the spray drying process for low-phenylalanine skim milk as a dietary supplement for phenylketonurics was studied. The effects of basic parameters including the inlet air temperature (100, 150 and 200 °C), feed flow rate (5, 10 and 15 mL min−1) and air flow rate (400, 600 and 800 L h−1) on the solubility and particle size of the low-phenylalanine skim milk powder were determined using response surface methodology. The optimum conditions have been obtained with inlet air temperature of 133 °C, feed flow rate of 5 mL min−1, and air flow rate of 800 L h−1. With the optimum parameters, the predicted values for the solubility and mean diameter were 95.33% and 5.34 μm, respectively, and experimentally were 94.36 ± 1.62% and 5.50 ± 0.44 μm, respectively. The experimental and predictive values were closely related showing predictive accuracy of the models.

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