Coupling the image analysis and the artificial neural networks to predict a mixing time of a pharmaceutical powder
| Autor: | Kamel Daoud, L Mouhi, N Guemras, Yassine Mahdi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Active ingredient
Engineering Artificial neural network business.industry Homogeneity (statistics) 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology computer.software_genre 01 natural sciences Field (computer science) 0104 chemical sciences ANN Image analysis Homogeneity Back-propagation algorithm multi-layer perceptron Software Multilayer perceptron Data mining 0210 nano-technology business Biological system computer Mixing (physics) Powder mixture |
| Zdroj: | Journal of Fundamental and Applied Sciences; Vol 8, No 3 (2016); 655-670 |
| ISSN: | 1112-9867 |
| Popis: | In recent years, different laboratories were interested in predicting the mixing time of a pharmaceutical powder. In fact, a nonhomogeneous mixture may lead to under dose and/or overdose of the active ingredient in the drug product. Our study is aimed toward using a new and revolutionary approach in the field of the processes “The Artificial Neural Networks” (ANN) by using the Neural Networks ToolboxTM derived from Matlab® software. The validation of the neural network was assumed by studying others mixing powder s and then we compared the experimental results to the data obtained by the neural network calculations. Experimental results were obtained from a non-destructive method (Image Analysis) which was used in order to characterize the homogeneity of powder mixture in a V-Blender as well as a Cubic Blender which are most used in the pharmaceutical industry.Keywords: ANN; Image analysis; Homogeneity; Back-propagation algorithm; multi-layer perceptron |
| Databáze: | OpenAIRE |
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