平板涂布机片剂涂布的人工神经网络建模

IF 2.3 4区 材料科学 Q2 Chemistry Journal of Coatings Technology and Research Pub Date : 2022-10-17 DOI:10.1007/s11998-022-00683-1
Assia Benayache, Lynda Lamoudi, Kamel Daoud
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引用次数: 1

摘要

我们的研究决定通过使用Matlab®软件衍生的神经网络工具箱,在制药涂层工艺领域使用新的革命性方法,即人工神经网络(ANN)。实验以水合Alfuzosin片剂为模型填料,以不同含量的Surelease水溶液为聚合物。研究了喷涂速率、气压、固含量、转鼓速度、装盘速度和喷涂时间等参数对涂层厚度、增重和变异系数CV的影响。利用人工神经网络对包衣片剂的性能进行评价,并以包衣工艺参数和包衣片剂的性能作为优化的依据,选择人工神经网络模型的最优结构。结果表明,最优的神经网络结构为隐藏层7个神经元,均方误差为3.515,决定系数接近1。每个自变量的相对重要性使用Garson方程进行量化。在本研究中,发现喷雾速率对片剂性能的影响最大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Artificial neural network modeling of tablet coating in a pan coater

Our study decided to use the new and revolutionary approach in the field of pharmaceutical coating processes called the artificial neural network (ANN) by using the neural networks toolbox derived from the Matlab® software. The experiments were performed using tablets of Alfuzosin Chlorhydrate as a model filler, and an aqueous solution of Surelease as a polymer in different contents. The various parameters that can affect coating thickness, weight gain, and the coefficient of variation CV, such as spray rate, air pressure, solid content, speed of the drum, pan loading, and time of coating, were studied. The properties of the coated tablets were evaluated using the ANN, and both the parameters of the coating process and the properties of the coated tablets were used as a basis for optimization, as well as the choice of the optimal structure of the ANN model. It was found that the best neural network architecture had 7 neurons in the hidden layer, with a mean square error of 3.515 and a determination coefficient of nearly 1. The relative importance of each independent variable was quantified using the Garson equation. In this study, spray rate was found to have the highest impact on the properties of tablets.

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来源期刊
Journal of Coatings Technology and Research
Journal of Coatings Technology and Research CHEMISTRY, APPLIED-MATERIALS SCIENCE, COATINGS & FILMS
CiteScore
4.40
自引率
8.70%
发文量
0
期刊介绍: Journal of Coatings Technology and Research (JCTR) is a forum for the exchange of research, experience, knowledge and ideas among those with a professional interest in the science, technology and manufacture of functional, protective and decorative coatings including paints, inks and related coatings and their raw materials, and similar topics.
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