Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L.

作者：J. Prakash Maran, V. Sivakumar, K. Thirugnanasambandham, R. Sridhar · 发表于：Alexandria Engineering Journal · 年份：2013 · DOI：10.1016/j.aej.2013.06.007 · 被引用次数：202 · 研究领域：Spectroscopy and Chemometric Analyses、Food Drying and Modeling、Postharvest Quality and Shelf Life Management

In this study, a comparative approach was made between artificial neural network (ANN) and response surface methodology (RSM) to predict the mass transfer parameters of osmotic dehydration of papaya. The effects of process variables such as temperature, osmotic solution concentration and agitation speed on water loss, weight reduction, and solid gain during osmotic dehydration were investigated using a three-level three-factor Box-Behnken experimental design. Same design was utilized to train a feed-forward multilayered perceptron (MLP) ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of root mean square error (RMSE), mean absolute error (MAE), standard error of prediction (SEP), model predictive error (MPE), chi square statistic (χ2), and coefficient of determination (R2) based on the validation data set. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.