Behroozi-Khazaei Nasser, Nasirahmadi Abozar
Department of Biosystems Engineering, University of Kurdistan, Sanandaj, Iran.
School of Agriculture, Food and Rural Development, Newcastle University, Newcastle upon Tyne, NE1 7RU UK.
J Food Sci Technol. 2017 Jul;54(8):2562-2569. doi: 10.1007/s13197-017-2701-x. Epub 2017 May 19.
In this study, milling recovery, head rice yield, degree of milling and whiteness were utilized to characterize the milling quality of Tarom parboiled rice variety. The parboiled rice was prepared with three soaking temperatures and steaming times. Then the samples were dried to three levels of final moisture contents [8, 10 and 12% (w.b)]. Modeling of process and validating of the results with small dataset are always challenging. So, the aim of this study was to develop models based on the milling quality data in parboiling process by means of multivariate regression and artificial neural network. In order to validate the neural network model with a little dataset, K-fold cross validation method was applied. The ANN structure with one hidden layer and Tansig transfer function by 18 neurons in the hidden layer was selected as the best model in this study. The results indicated that the neural network could model the parboiling process with higher degree of accuracy. This method was a promising procedure to create accuracy and can be used as a reliable model to select the best parameters for the parboiling process with little experiment dataset.
在本研究中，利用碾磨回收率、整精米率、碾磨度和白度来表征塔罗姆（Tarom）半熟米品种的碾磨品质。半熟米采用三种浸泡温度和蒸煮时间制备。然后将样品干燥至三种最终水分含量水平[8%、10%和12%（湿基）]。使用小数据集对过程进行建模并验证结果一直具有挑战性。因此，本研究的目的是通过多元回归和人工神经网络，基于半熟化过程中的碾磨品质数据建立模型。为了用少量数据集验证神经网络模型，应用了K折交叉验证方法。本研究选择具有一个隐藏层且隐藏层中有18个神经元的Tansig传递函数的人工神经网络结构作为最佳模型。结果表明，神经网络能够以更高的精度对半熟化过程进行建模。该方法是一种有前景的创建高精度模型的方法，可作为一种可靠的模型，在实验数据集较少的情况下选择半熟化过程的最佳参数。
