Prediction of convective heat transfer coefficient during deep-fat frying of pantoa using neurocomputing approaches

Abstract

Deep-fat frying (DFF) is the major processing step in preparation of pantoa, a popular Indian dairy sweetmeat. In this study, the dough for pantoa was rolled into balls of 15 g, and fried in sunflower oil at 125, 135 and 145 °C for 8 min. Convective heat transfer coefficient, which defines the heat transfer characteristics of the product during DFF, was determined using one-dimensional transient heat conduction equation as 92.71–332.92 W·m− 2·K− 1. Neurocomputing techniques such as connectionist models and adaptive neurofuzzy inference system (ANFIS) were compared vis-à-vis multiple linear regression (MLR) models for prediction of heat transfer coefficient. A back-propagation algorithm with Bayesian regularization optimization technique was employed to develop connectionist models while the ANFIS model was based on Sugeno-type fuzzy inference system. Both connectionist and ANFIS models exhibited superior prediction abilities than the classical MLR model. Amongst the three approaches, the hybrid ANFIS model with triangular membership function and frying time and temperature as input factors gave the best fit of convective heat transfer coefficient with R2 as high as 0.9984 (99.84% accuracy) and %RMS value of 0.1649.