1997
DOI: 10.1007/bf02496374
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Multivariate evaluation of the separation performance in micellar electrokinetic capillary chromatography of peptides: Experimental screening

Abstract: SummaryCritical parameters for the separation of enkephalin related peptides by micellar electrokinetic chromatography were identified by using statistical experimental design. Nine experimental variables: micelle concentration, pH of the background electrolyte, addition of organic modifier to the background electrolyte, injection length, temperature of the capillary, applied voltage, ionic strength of the buffer, the composition of the injected solution and stacking were investigated in a twolevel fractional … Show more

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Cited by 10 publications
(6 citation statements)
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“…A) From the CCD according to Table I B) From the CCD without axial points tion to the micelles, c.f. [12]. The peptides studied all have their isoelectric point (pI) at 6.0 except for the peptide containing arginine which has pI > 9.0 [14].…”
Section: Resultsmentioning
confidence: 99%
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“…A) From the CCD according to Table I B) From the CCD without axial points tion to the micelles, c.f. [12]. The peptides studied all have their isoelectric point (pI) at 6.0 except for the peptide containing arginine which has pI > 9.0 [14].…”
Section: Resultsmentioning
confidence: 99%
“…The level of the factors were based on a previous experimental screening [12], the experimental domain was defined according to Table I. The SDS concentration was varied from 30 to 60 mM, the acetonitrile percentage between 5-15 % v/v, the ionic strength of the buffer between 0.02 to 0.04, the injected plug length was varied from 1.9 (3 s 40 mbar) to 4.7 min (7 s 40 mbar), the temperature between 25 and 40 ~ and the composition of the injected solution was without or with micelles.…”
Section: Mekc Conditions Used For the Optimizationmentioning
confidence: 99%
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“…Experimental design offers an efficient alternative for identifying the conditions yielding the best resolution [18][19][20]. Many factors can be screened simultaneously to determine which one has a significant effect on the separation.…”
Section: Optimization and Robustnessmentioning
confidence: 99%
“…However, this step-by-step approach, although widely used, involves a large number of independent runs and, since multiple interacting parameters determine the observed separation, a multivariate optimization was considered necessary [12,[15][16][17][18][19][20]. In particular experimental design offers an efficient route for identifying the zone in factor space for the best resolution in CE [21][22][23][24][25][26][27]. It allows a large number of factors to be screened simultaneously to determine which of them has a significant effect on the separation.…”
Section: Introductionmentioning
confidence: 99%