Adaptive neuro-fuzzy interface system and neural network modeling for the drying kinetics of instant controlled pressure drop treated parboiled rice.

Abstract

Hot air drying kinetics of paddy grains during instant controlled pressure drop (ICPD) assisted parboiling process and its impact on the quality and micro-structural properties of milled rice were investigated. Among five mathematical models, Midilli model showed best fitted outcomes for prediction of adequate drying behavior. For the mapping of moisture ratio (MR) as a function of treatment pressure (TP), decompressed state duration (DD) and drying time (DT), artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) were applied. ANFIS model (5-5-5) with Gaussian membership function demonstrated best performance when contrasted with 3-5-1 ANN architecture. Effective diffusivity of the drying process varied from 2.8 × 10-09 to 7.0 × 10-09 m2/s with the increase of TP and DD. In comparison of quality parameters with the variation of TP and DD, positive impacts on head rice yield (HRY), redness (a*) and yellowness (b*) values and negative consequences on cooking time (CT) and brightness (L*) value were observed. The outcomes additionally uncovered that parboiled rice obtained at 0.6 MPa TP, indicated best quality in terms of improved process performance, HRY, CT, color and micro-structural properties.