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Artificial Neural Network Modeling of Water Activity: a Low Energy Approach to Freeze Drying

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Abstract

A method for reducing the energy consumption during freeze drying has been proposed. Water activity variation with time has been explored for button mushroom (Agaricus bisporus L.). The effect of primary and secondary drying temperatures on water activity was found significant (p < 0.05) as compared to sample thickness and pressure. The economics of the process showed that an energy reduction up to 34.9% could be achieved if the final water activity was constrained at 0.6. Artificial neural network tool has been used to develop a model for predicting the water activity precisely for a given combination of time, initial moisture content, vacuum pressure, sample thickness, and primary and secondary drying temperatures. The model-predicted values were found to be in good agreement (R = 0.97) with the experimental data. The model developed is expected to extend its aid in energy reduction for freeze drying of other food products.

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Abbreviations

a w :

Water activity

b ij , b ij * :

Network bias

H I ij :

Hidden layer input

H O ij :

Hidden layer output

I i :

Input

max(x):

Maximum value in x

min(x):

Minimum value in x

O O 1 :

Final output of the ANN infrastructure

O I 1 :

Processed input signal to the output neuron

T :

Set target for backpropagation (error control)

v ij , w ij , X ij :

Connection weights

x :

Data set

x norm :

Normalized data set

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Acknowledgements

The authors express their heartfelt gratitude to the Mushroom Research Center (MRC), G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India, for cultivating button mushrooms in their facility and making them available for this work. The authors extend their thanks to Dr. U.C. Lohani, Er. Anurag Kushwaha, and Er. Ashish Kumar for providing their valuable assistance for successful completion of this work.

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Authors and Affiliations

  1. Department of Post Harvest Process and Food Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India

    Ayon Tarafdar, Navin Chandra Shahi, Anupama Singh & Ranjna Sirohi

  2. 17E/361 A, Chopasni Housing Board, Jodhpur, Rajasthan, 342008, India

    Ayon Tarafdar

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  1. Ayon Tarafdar
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  2. Navin Chandra Shahi
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  3. Anupama Singh
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  4. Ranjna Sirohi
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Correspondence to Ayon Tarafdar.

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Tarafdar, A., Shahi, N.C., Singh, A. et al. Artificial Neural Network Modeling of Water Activity: a Low Energy Approach to Freeze Drying. Food Bioprocess Technol 11, 164–171 (2018). https://doi.org/10.1007/s11947-017-2002-4

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  • DOI: https://doi.org/10.1007/s11947-017-2002-4

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