NDLI: Comparison of migration modeling in micellar electrokinetic chromatography by linear regression and by use of an artificial neural network

Content Provider	Springer Nature Link
Author	Metting, H. J. Zomeren, P. V. Ley, C. P. Coenegracht, P. M. J. Jong, G. J.
Copyright Year	2000
Abstract	The concentrations of modifier (methanol or acetonitrile) and surfactant (sodium dodecyl sulfate, SDS) in the running buffer are important factors influencing the mobility of analytes in micellar electrokinetic chromatography (MEKC). Response surfaces of the effective mobility can be used to predict mobility as a function of the buffer composition.This paper describes the modeling, by multiple linear regression and by use of a back-propagation neural network, of the response surfaces of the effective mobility of cocaine and related compounds, of fluvoxamine and its possible impurities, and of a mixture of alkylphenones. Special attention has been paid to selection of the proper order of the regression models, to selection of the proper network configuration, and to the predictive quality of the models. The regression models were selected by backward elimination of non-significant terms starting with a full second-order polynomial model. The proper network configuration was selected by finding the optimum number of hidden neurons by lateral inhibition and subsequent determination of the optimum number of training cycles by a cross-validation method.On the basis of the multiple determination coefficient,R 2, the descriptive performances of the neural network and the regression models are almost the same. However, the predictive performance of the regression models and the neural network models are approximately equal for migration in MEKC. The relative prediction error of the effective mobility is ca 2.5% for cocaine and related compounds, 1 – 7% for the alkylphenones, and 1.5 – 2.2% for fluvoxamine and its possible impurities; this is approximately the same order of magnitude, or slightly higher, than the relative standard deviation of the replicate measurements.
Starting Page	607
Ending Page	613
Page Count	7
File Format	PDF
ISSN	00095893
Journal	Chromatographia
Volume Number	52
Issue Number	9-10
e-ISSN	16121112
Language	English
Publisher	Vieweg Verlag
Publisher Date	2000-01-01
Publisher Place	Wiesbaden
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Micellar electrokinetic chromatography Multiple linear regression Neural networks Response surface modeling Analytical Chemistry Organic Chemistry Pharmacy Biochemistry Plant Sciences Measurement Science, Instrumentation
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Analytical Chemistry Biochemistry Clinical Biochemistry

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Separation ofPodophyllum lignans by micellar electrokinetic capillary chromatography (MECC)

Separation of anabolic steroids by micellar electrokinetic capillary chromatography

Method development for Micellar Electrokinetic Chromatography

Separation of aromatic sulfonate compounds by coelectroosmotic micellar electrokinetic chromatography

Multivariate evaluation of the separation performance in micellar electrokinetic capillary chromatography of peptides: Optimization

Comparison of selectivity between HPLC and micellar electrokinetic chromatography

Determination of ethinylestradiol and gestodene in oral contraceptives by micellar electrokinetic chromatography

Determination of 20-hydroxyecdysone content by thin-layer chromatography and micellar electrokinetic chromatography

Micellar electrokinetic capillary chromatography for selected tropane alkaloid analysis in plant extract

Comparison of migration modeling in micellar electrokinetic chromatography by linear regression and by use of an artificial neural network

Similar Documents

Separation ofPodophyllum lignans by micellar electrokinetic capillary chromatography (MECC)

Separation of anabolic steroids by micellar electrokinetic capillary chromatography

Method development for Micellar Electrokinetic Chromatography

Separation of aromatic sulfonate compounds by coelectroosmotic micellar electrokinetic chromatography

Multivariate evaluation of the separation performance in micellar electrokinetic capillary chromatography of peptides: Optimization

Comparison of selectivity between HPLC and micellar electrokinetic chromatography

Determination of ethinylestradiol and gestodene in oral contraceptives by micellar electrokinetic chromatography

Determination of 20-hydroxyecdysone content by thin-layer chromatography and micellar electrokinetic chromatography

Micellar electrokinetic capillary chromatography for selected tropane alkaloid analysis in plant extract

Comparison of migration modeling in micellar electrokinetic chromatography by linear regression and by use of an artificial neural network