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Freeze-drying behaviour prediction of button mushrooms using artificial neural network and comparison with semi-empirical models

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Abstract

The application of artificial neural networks (ANN) in the freeze-drying of button mushrooms has been investigated. Networks with a single hidden layer, different training algorithms and complexity in terms of the number of neurons were evaluated for identifying the best ANN infrastructure. Moisture content, moisture ratio and drying rate were taken as output drying parameters for which ANN models provided an overall correlation coefficient (R) of 0.994, 0.991 and 0.992, respectively. The predictive efficiency of ANN was compared to semi-empirical models. Coefficients for semi-empirical models of moisture ratio were determined. Logarithm model gave the best fit (R2 = 0.985) for moisture ratio prediction but with larger mean square error and lower correlation than ANN model. The study highlights that ANN models with low complexity can be developed to precisely predict drying behaviour of biological materials while providing comparable and even superior results to that obtained from available semi-empirical drying models.

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Abbreviations

a, b, c, k, n, k 0, k 1, k 2 :

Model coefficients

b j, b k :

Weight bias of jth and kth neuron

d.b.:

Dry basis

p :

Number of explanatory variables (excluding constants)

y i :

Observed data

\(\overline{{y_{1} }}\) :

Mean of observed data

\(\widehat{{y_{1} }}\) :

Predicted data

t :

Time

v jk :

Weight of connection from jth neuron to kth neuron

w ij :

Weight of connection from ith neuron to jth neuron

w.b.:

Wet basis

DR:

Drying rate

H I j :

Input signal to jth neuron of hidden layer

H O j :

Output signal from jth neuron of hidden layer

M 0 :

Initial moisture content

M e :

Equilibrium moisture content

MR:

Moisture ratio

M t :

Moisture content at time t

N :

Sample size

O I k :

Input signal to kth neuron of output layer

O O k :

Output signal from kth neuron of output layer

PDT:

Primary drying temperature

R 2 :

Coefficient of determination

R 2adj :

Adjusted coefficient of determination

SDT:

Secondary drying temperature

ST:

Sample thickness

W f :

Bone dry sample weight

W t :

Sample weight at time t

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Acknowledgements

The authors wish to thank Ms. Ranjna Sirohi, Mr. Mohd. Ishfaq Bhat, Mr. Anurag Kushwaha, Department of Post-Harvest Process and Food Engineering and Ms. Himani Joshi, College of Home Science, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, for their valuable assistance and suggestions for carrying out this study. The authors extend their gratitude to MRC, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, for growing and providing fresh button mushrooms for conducting the research work. Finally, the first author expresses admiration for the constant support of Ms. Ranjna Sirohi throughout the work and would like to ask her: Will you marry me?

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Authors and Affiliations

  1. Department of Food Engineering, National Institute of Food Technology Entrepreneurship and Management, Kundli, Sonipat, Haryana, 131028, India

    Ayon Tarafdar

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

    Ayon Tarafdar, Navin Chandra Shahi & Anupama Singh

  3. Jodhpur, Rajasthan, 342008, India

    Ayon Tarafdar

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  1. Ayon Tarafdar
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Correspondence to Ayon Tarafdar.

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Tarafdar, A., Shahi, N.C. & Singh, A. Freeze-drying behaviour prediction of button mushrooms using artificial neural network and comparison with semi-empirical models. Neural Comput & Applic 31, 7257–7268 (2019). https://doi.org/10.1007/s00521-018-3567-1

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