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| Author(s): |
Márcia Cristina Breitkreitz
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | Campinas, SP. |
| Institution: | Universidade Estadual de Campinas (UNICAMP). Instituto de Química |
| Defense date: | 2007-06-07 |
| Examining board members: |
Roy Edward Bruns;
Ieda Spacino Scarminio;
Carla Beatriz Grespan Bottoli
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| Advisor: | Isabel Cristina Sales Fontes Jardim; Roy Edward Bruns |
| Abstract | |
The aim of this work was to develop combined statistical models including the stationary phase (SP) as process variables and different compositions of the mobile phase (MP) as mixture variables in order to describe the influence of each type of variable as well as their interactions for the separation of compounds in two samples sets: one containing ten neutral compounds and another containing eleven pesticides. The experiments required to determine the coefficients of the models were carried out according to a split-plot approach, in which the stationary phases, C 8 or C 18 were considered as main-plots and the mobile phase compositions as sub-plots. The results were treated according to the split-plot approach and also supposing a completely random setup. The results provided by an objective function were compared to those obtained by Derringer¿s desirability functions constructed with simple chromatographic criteria such as resolution and relative retention factors as responses. The models were evaluated by means of Analysis of Variance, regarding regression significance and lack of fit. In order to describe the quality of the separation of the compounds in the two mixtures, the desirability procedure was preferred over the objective functions because the responses used in the latter were, in fact, functions of the stationary and mobile phases. The models combined into a global desirability function allowed the best conditions to the separation of all compounds to be found, without loss of information on the individual peak separation. All models presented predictive capabilities for the responses evaluated with none or little lack of fit. Although the experiments were carried out according to a split-plot approach, no significant differences were found in coefficient errors comparing to the complete random approach, which can be explained based on the low main-plot error (AU) | |