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Selection of an optimal method for the preparation of dual loaded flavono polymeric nanoparticle using analytical hierarchy process.
Article in English | IMSEAR | ID: sea-167968
ABSTRACT
In this study, analytical hierarchy process was used to select an optimal method for the preparation of dual loaded flavono polymeric nanoparticles. Analytical hierarchy process involves structuring multiple choice criteria into a hierarchy, assessing the relative importance of criteria, comparing alternatives for each criterion and determining an overall ranking of the alternatives. Hierarchy model was developed with the goal in the first level, 10 criteria in the second level and methods for the preparation of polymeric nanoparticles in the third level. To assess the relative importance of criteria, all criteria were compared with each other using Saaty’s scale. To compare the methods, all the methods for the preparation of polymeric nanoparticles were compared with each other for each criterion using Saaty’s scale, which leads to the formation of pair-wise comparison matrixes and consistency ratio was calculated for the each pair-wise comparison matrix. The study result showed that the consistency ratio of each pair-wise comparison matrix were well within acceptable limits. Of 10 criteria, reproducible results received the maximum overall priority weight followed by desirable size. Of 10 methods, nanoprecipitation method received the maximum overall priority weight followed by supercritical fluid technology. Analytical hierarchy process has identified reproducible results as criteria preference and nanoprecipitation as an optimal method for the preparation of dual loaded flavono polymeric nanoparticles. The study concludes that the analytical hierarchy process has played a vital role in selecting an optimal method for the preparation of dual loaded flavono polymeric nanoparticles.

Full text: Available Index: IMSEAR (South-East Asia) Type of study: Prognostic study Language: English Year: 2014 Type: Article

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Full text: Available Index: IMSEAR (South-East Asia) Type of study: Prognostic study Language: English Year: 2014 Type: Article