Response surface methodology desirability

Soy protein isolate shows an additive or synergistic interaction with potassium that may be successfully exploited to design gel products with desired characteristics. To this end the response surface methodology proved to be an excellent tool. 

For instance, Yadav and Ahuja prepared nanoparticles using gum cordia as the polymer and to evaluate them for ophthalmic delivery of fluconazole. A w/o/w emulsion containing fluconazole and gum cordia in aqueous phase, methylene chloride as the oily phase, and dioctyl sodium sulfosuccinate and polyvinyl alcohol as the primary and secondary emulsifiers, respectively, were cross-linked by the ionic gelation technique to produce a fluconazole-loaded nanoreservoir system. The formulation of nanoparticles was optimized using response surface methodology. Multiple response simultaneous optimizations using the desirability approach were used to find optimal experimental conditions. The optimal conditions were found to be concentrations of gum cordia (0.85%, w/v), di-octyl sodium sulfosuccinate (9.07%, w/v), and fluconazole (6.06%, w/v). On comparison of the optimized nanosuspension formulation with commercial formulation, it was found to provide comparable in vitro corneal permeability of... 

Response surface methodology (RSM) is utilized to develop mathematical relationships as well as for searching out optimal EMM process parameters setting to achieve the desired level of micro-machining performance criteria. EMM process criteria are measured during linear microchannel generation on copper workpiece, after each set of experimental mn based on experimental planning. The actual values of factors and their levels utilized for the RSM are listed in Table 8.3. 

Combining the response surface methodology and multicriteria optimisation based on an overall desirability function is an efficient method of the solution of that problem. 

Statistical design of experiment (DOE) is an efficient procedure for finding the optimum molar ratio for copolymers having the best property profile. Based on the concepts of response-surface (RS) methodology, developed by Box and Wilson [11], there are four models or polynominals (Table III) useful in our study. For three components, in general, if there are seven to nine experimental data points, the linear, quadratic and special cubic will be applicable for use in predictions. If there are ten or more data points, the full cubic model will also be applicable. At the start of the effort, one prepares a fair number of copolymers with different AA IA NVP ratios and tests for a property one wishes to optimize, with the data fit to the statistical models. Based on the models, new copolymers, with different ratios, are prepared and tested for the desired property improvement. This type procedure significantly lowers the number of copolymers that needs to be prepared and evaluated, in order to identify the ratio needed to give the best mechanical property. 

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