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COMPUTER-AIDED OPTIMIZATION OF MICROEXTRACTION TECHNIQUE
MIROSLAVA BURSOVÁ*, RADOMÍR ABALA
Charles University in Prague, Faculty of Science, Department of Analytical Chemistry, Albertov 6, 128 43 Prague 2, Czech
Republic.
bursova.mirka@seznam.cz
1. Introduction
The presented work deals with a facilitation of an optimization process of general liquid-liquid microextraction
technique (LLME). For that purposes, a comparison of two commercial software packages (NCSS and Design Expert) in
their default settings was used and compared to demonstrate their ability to provide experimental designs (plans), to analyze
and evaluate the experimental results (responses) by a set of mathematical and statistical methods generally called response
surface methodology (RSM) [1].
Response surface methodology is based on the fit of a polynomial equation to the experimental data, which must
describe the behavior of a data set with the objective of making statistical prevision [2]. RSM is a general approach for design
of experiments which effectively reduce the method development time and costs. The main advantage of the RSM compared
to a widely used one-factor-at-a-time approach (OFAT, one parameter is changed while the others are fixed during
optimization) is that only the most important experimental parameters are selected and optimized, and that more than one
parameter is changed simultaneously in one experiment according to the software proposed plan of experiments. Generally,
RSM consists of several consecutive steps: screening, modeling and optimization [3].
Before using the RSM, it is important to define the analytical goals and select the appropriate parameters and responses
[2]. As an example, we selected a procedure of the microextraction of 10 ml of aqueous solution of 8 analytes (toluene,
ethylbenzene, mesitylene, phenol, nitrobenzene, n-octanol, naphthalene and dimethylphthalate, all 1 g l -1 ) by several
hundreds l of heptane (internal standard in heptane was methylhexanedecanoate) in 15 ml glass vial. The organic phase was
analyzed by fast-GC method with FID and the analyte peak areas determined.
The selected experimental parameters of interest were the extraction time, the volume of extraction solvent, the addition
of salt, the stirring rate and the diameter of extraction vial. The extraction was performed at ambient temperature. As the
analytical response, either the peak areas of single analytes or the sum of peak areas of all analytes measured are used.
Response surface methodology
Screening
Reduced factorial designs are commonly recommended for the screening to limit the large number of experimental
parameters to be optimized. Generally, Plackett-Burman design is applied for screening of independent experimental
parameters in order to determine the parameters that significantly affect the extraction efficiency. The Plackett-Burman
design assumes that the interactions among the selected parameters can be completely ignored so, their main influence on the
analytical response is evaluated with a considerably reduced number of experiments [4].
Modeling
In this step, experiments are designed with the purpose of modeling the measured responses as a function of significant
parameters, usually by a second-order polynomial fit [3]. One of several possible designs of experiments is a central
composite design (CCD) which involves two–level full factorial, fractional factorial and star design [1, 4]. In contrast to the
Zborník príspevkov
z 18. medzinárodnej vedeckej konferencie
"Analytické metódy a zdravie loveka", ISBN 978-80-969435-7-9
- 92 -
hotel Falkensteiner, Bratislava
11. - 14. 10. 2010