Mahfeli Mandana, Zarein Mohammad, Zomorodian Aliasghar, Khafajeh Hamid
Biosystems Engineering Department Tarbiat Modares University Tehran Iran.
Department of Biosystems Engineering Shiraz University Shiraz Iran.
Food Sci Nutr. 2022 Jul 2;10(10):3501-3514. doi: 10.1002/fsn3.2953. eCollection 2022 Oct.
Parboiling is a type of heat pretreatment used in rice processing to reach higher head rice yield and improve the nutrition properties of raw rice. In this research, the goals were prediction and determination of optimum conditions for parboiled rice processing using the response surface method (RSM) as well as modeling the output values by linear regression (LR) and artificial neural networks (ANN). The parameters including steaming time (0, 5, 10, and 15 min), dryer type (solar and continuous dryers), and drying air temperature (35, 40, and 45°C) were employed as input values. In addition, the breakage resistance (BR) and head rice yield (HRY) were selected as output values. The ANN-based nonlinear regression, the multi-layer perceptron (MLP), and the radial basis function (RBF) have been developed to model the process parameters, as well as the central composite design (CCD) was conducted for optimization of BR and HRY values. The outputs of RBF network have been successfully applied to predict higher coefficient of determination of BR and HRY as 0.989 and 0.986, respectively, indicating the appropriateness of the model equation in predicting head rice yield and breakage resistance when the three processing variables (steaming time, dryer type, and drying air temperature) are mathematically combined. Also, the lower root mean square error (RMSE) was obtained for each one as 0.043 and 0.041. The optimum values of BR and HRY were obtained as 12.80 N and 67.3%, respectively, at 9.62 min and 36.9°C for a solar dryer with a desirability of 0.941. In addition, the same values were obtained as 14.50 N and 72.1%, respectively, at 8.77 min and 37.0°C for a continuous dryer with a desirability of 0.971.
蒸煮是大米加工中使用的一种热预处理方式，目的是提高整精米率并改善原米的营养特性。在本研究中，目标是使用响应面法（RSM）预测和确定蒸煮米加工的最佳条件，并通过线性回归（LR）和人工神经网络（ANN）对输出值进行建模。将包括蒸煮时间（0、5、10和15分钟）、干燥机类型（太阳能干燥机和连续干燥机）以及干燥空气温度（35、40和45°C）的参数用作输入值。此外，选择抗破损性（BR）和整精米率（HRY）作为输出值。已开发基于ANN的非线性回归、多层感知器（MLP）和径向基函数（RBF）来对工艺参数进行建模，同时进行中心复合设计（CCD）以优化BR和HRY值。RBF网络的输出已成功应用于预测BR和HRY的更高决定系数，分别为0.989和0.986，这表明当将三个加工变量（蒸煮时间、干燥机类型和干燥空气温度）进行数学组合时，模型方程在预测整精米率和抗破损性方面的适用性。此外，每个变量的均方根误差（RMSE）较低，分别为0.043和0.041。对于太阳能干燥机，在9.62分钟和36.9°C时，BR和HRY的最佳值分别为12.80 N和67.3%，可取性为0.941。此外，对于连续干燥机，在同样的8.77分钟和37.0°C时，BR和HRY的最佳值分别为14.50 N和72.1%，可取性为0.971。
