Artificial Neural Network Modeling of Osmotic Dehydration Mass Transfer Kinetics of Fruits

Abstract

Artificial neural network (ANN) models were developed for the prediction of transient moisture loss (ML) and solid gain (SG) in osmotic dehydration of fruits using process kinetics data from the literature. ANN models for ML and SG were developed based on data over a broad range of operating conditions and ten common processing variables: temperature and concentration of osmotic solution, immersion time, initial water and solid content of the fruit, porosity, surface area, characteristic length, solution-to–fruit mass ratio, and agitation level. The trained models were able to accurately predict the outputs with associated regression coefficients (r) of 0.96 and 0.93, respectively, for ML and SG. These ANN models performed much better than those obtained from linear multivariate regression analysis. The large number of process variables and their wide ranges considered along with their easy implementation in a spreadsheet make them very useful and practical for process design and control.

Keywords:

• Artificial neural network
• Mass transfer
• Modeling
• Moisture loss
• Osmotic dehydration
• Solids gain

ACKNOWLEDGMENTS

The fellowship for doctoral studies offered to the first author (C.I. Ochoa-Martínez) from COLCIENCIAS (Instituto Colombiano para el desarrollo de la Ciencia y la Tecnología Francisco José Caldas) is gratefully acknowledged. This research was also supported by funds from the Natural Sciences and Engineering Research Council of Canada.

Notes

*MSE (mean square error), NMSE (normalized mean square error), MAE (mean absolute error).