67
Views
11
CrossRef citations to date
0
Altmetric
Electrophoresis

Experimental Design to Investigate Factors Affecting Capillary Zone Electrophoresis

, &
Pages 2485-2499 | Received 04 May 2004, Accepted 08 Jun 2004, Published online: 22 Aug 2007
 

Abstract

An experimental design approach is described to evaluate the main electrophoretic parameters involved in neuropeptide separation by capillary zone electrophoresis coupled to ultraviolet detection. Response surface methodology based on a three‐level, three‐variable Box–Behnken design was used for optimization with respect to buffer pH, buffer concentration, and applied voltage. The optimum conditions of these variables were predicated by using a second‐order polynomial model fitted to the results obtained by applying the Box–Behnken design. The methodology identifies the principal experimental factors, which have significant effects in the separation. The optimum conditions were 100 mM phosphate buffer pH 2.5 as the background electrolyte and an applied voltage of 6 kV. Verification experiments were performed under optimal conditions, which yielded 98% of the predicated efficiency.

Acknowledgments

The authors wish to thank Miss Bai for her helpful comments on the manuscript. This work was supported in part by a grant from KIST (Studies on metabolomics, 2E17800; Systems Biology Research Grant 2N26170) and a grant from the Brain Science Research Group (2N25540), Ministry of Science and Technology.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 768.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.