Prediction of Mass Transfer during Osmotic Dehydration of Black Fig Fruits (Ficus carica) in Ternary Systems: Comparison of Response Surface Methodology and Artificial Neural Network

Document Type : Research Article

Authors

1 Agricultural Engineering Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Fars Province, Iran

2 Agricultural Engineering Research Department, Fars Agricultural and Natural Resources Research and Education Center, AREEO, Fars, Shiraz, Iran.

3 AREEO

Abstract

Osmotic dehydration of fig fruits (cv. Sabz) in ternary solution of water, sucrose and sodium chloride at different solution concentrations, temperature and process durations were analyzed. A comparative approach was made between artificial neural network (ANN) and response surface methodology (RSM) to predict the mass transfer parameters. Results showed that all independent variables positively decreased the weight meaning that increasing each factor resulted in increasing weight loss and this relationship was linear. Osmo-dehydrated figs had better quality compared to samples without osmosis. All four independent variables explained 94% of the weight loss, 90% moisture content reduction and 89% of the solid gain. The determined optimum processing conditions were temperature of 60°C, sucrose concentration of 70%, sodium chloride concentration of 5% and immersion time of 5h. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.

Graphical Abstract

Prediction of Mass Transfer during Osmotic Dehydration of Black Fig Fruits (Ficus carica) in Ternary Systems: Comparison of Response Surface Methodology and Artificial Neural Network

Highlights

  • Use of a ternary osmotic solution enhances the mass transfer when compared to binary osmotic solution; so it is proposed for the industry.
  • Optimized conditions for osmotic dehydration of fig fruits are temperature of 60°C, sucrose concentration of 70%, sodium chloride concentration of 5% and immersion time of 5h.
  • ANN model are capable of better predictions of ML, WR, and SG within the range comparing to RSM model.
  • A well-trained ANN model is more accurate in prediction of ML, WR, and SG than RSM model.

Keywords

Main Subjects

References

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Innovative Food Technologies
Volume 11, Issue 1
November 2023
Pages 61-75

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History

  • Receive Date: 03 March 2023
  • Revise Date: 28 April 2023
  • Accept Date: 05 June 2023

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