Kachrimanis K, Karamyan V, Malamataris S
Department of Pharmaceutical Technology, School of Pharmacy, University of Thessaloniki, Thessaloniki 54124, Greece.
Int J Pharm. 2003 Jan 2;250(1):13-23. doi: 10.1016/s0378-5173(02)00528-8.
Effects of micromeritic properties (bulk, tapped and particle density, particle size and shape) on the flow rate through circular orifices are investigated, for three pharmaceutical excipients (Lactose, Emcompress and Starch) separated in four sieve fractions, and are modeled with the help of artificial neural networks (ANNs). Eight variables were selected as inputs and correlated by applying the Spearman product-moment correlation matrix and the visual component planes of trained Self-Organizing Maps (SOMs). Back-propagation feed-forward ANN with six hidden units in a single hidden layer was selected for modeling experimental data and its predictions were compared with those of the flow equation proposed by. It was found that SOMs are efficient for the identification of co-linearity in the input variables and the ANN is superior to the flow equation since it does not require separate regression for each excipient and its predictive ability is higher. Besides the orifice diameter, most influential and important variable was the difference between tapped and bulk density. From the pruned ANN an approximate non-linear model was extracted, which describes powder flow rate in terms of the four network's input variables of the greatest predictive importance or saliency (difference between tapped and bulk density (x(2)), orifice diameter (x(3)), circle equivalent particle diameter (x(4)) and particle density [equation in text].
研究了三种药用辅料（乳糖、Emcompress和淀粉）的微粉特性（松密度、振实密度和颗粒密度、粒径和形状）对通过圆形孔口的流速的影响，这三种辅料分为四个筛分级分，并借助人工神经网络（ANN）进行建模。选择八个变量作为输入，并通过应用斯皮尔曼积矩相关矩阵和训练后的自组织映射（SOM）的可视化分量平面进行关联。选择在单个隐藏层中具有六个隐藏单元的反向传播前馈人工神经网络对实验数据进行建模，并将其预测结果与所提出的流动方程的预测结果进行比较。结果发现，自组织映射对于识别输入变量中的共线性是有效的，并且人工神经网络优于流动方程，因为它不需要对每种辅料进行单独回归，并且其预测能力更高。除了孔口直径外，最具影响力和重要的变量是振实密度和松密度之间的差异。从修剪后的人工神经网络中提取了一个近似非线性模型，该模型根据四个预测重要性或显著性最高的网络输入变量（振实密度和松密度之间的差异（x(2)）、孔口直径（x(3)）、圆形等效粒径（x(4)）和颗粒密度[文中方程]）来描述粉末流速。
