Neural network and computational fluid dynamics modeling for the gelatinization kinetics of instant controlled pressure drop treated parboiled rice

Abstract

AbstractEffects of treatment pressure (TP) and treatment time (TT) on the degree of gelatinization (DG) and its impact on the quality attributes of instant controlled pressure drop (ICPD) treated parboiled rice were investigated. Water diffusion and gelatinization kinetics were established for controlling the quality attributes of parboiled rice. Fick's second law was implemented for the evaluation of water diffusion kinetics. DG was fitted with first order reaction kinetics, which showed a rising trend of reaction rate constant from 0.03 to 0.05 s−1 with the variation of TP. Simulation of gelatinization temperature front and its effect on the gelatinization kinetics were modeled by applying computational fluid dynamics (CFD) approach. Process modeling of DG as a function of TP, TT, and moisture content (MC) was accomplished on the basis of 3‐7‐1 artificial neural network (ANN) architecture. Parboiled rice treated at 0.6 MPa TP showed the best quality in terms of broken percentage, ease of cooking, micro‐structural characteristics, and improved cooking and pasting properties.Practical applicationInstant controlled pressure drop (ICPD) treated parboiling process is an innovative approach for producing rice with improved quality attributes. Investigation of water diffusion and gelatinization kinetics is a vital aspect in order to control the milling quality of ICPD treated parboiled rice. Further, mapping of gelatinization temperature front is also necessary in order to control DG with the variation of ICPD treatment pressure and time. Therefore, this study was conducted to enumerate the effects of water diffusion and gelatinization kinetics on the quality attributes of ICPD treated parboiled rice.