Effects 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.

中文翻译：

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神经网络和计算流体动力学模型对速控压煮半熟大米糊化动力学的影响

研究了处理压力（TP）和处理时间（TT）对糊化度（DG）的影响及其对速控压降（ICPD）处理的半熟大米品质属性的影响。建立了水扩散和糊化动力学以控制煮熟米的质量属性。菲克第二定律用于评估水扩散动力学。DG符合一级反应动力学，反应速率常数从0.03上升到0.05 s ^-1随着TP的变化。糊化温度前沿的模拟及其对糊化动力学的影响是通过应用计算流体动力学（CFD）方法进行建模的。DG作为TP，TT和水分含量（MC）的函数的过程建模是基于3-7-1人工神经网络（ANN）架构完成的。在0.6 MPa TP下处理的半熟大米在破碎率，易烹饪性，微观结构特征以及改善的烹饪和粘贴特性方面显示出最佳品质。