276 related articles for article (PubMed ID: 18435511)
1. Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine-bonded stationary phase in hydrophilic interaction chromatography.
Quiming NS; Denola NL; Saito Y; Jinno K
J Sep Sci; 2008 May; 31(9):1550-63. PubMed ID: 18435511
[TBL] [Abstract][Full Text] [Related]
2. Development of retention prediction models for adrenoreceptor agonists and antagonists on a polyvinyl alcohol-bonded stationary phase in hydrophilic interaction chromatography.
Quiming NS; Denola NL; Samsuri SR; Saito Y; Jinno K
J Sep Sci; 2008 May; 31(9):1537-49. PubMed ID: 18428191
[TBL] [Abstract][Full Text] [Related]
3. Retention prediction modeling of ginsenosides on a polyvinyl alcohol-bonded stationary phase at subambient temperatures using multiple linear regression and artificial neural network.
Quiming NS; Denola NL; Soliev AB; Saito Y; Jinno K
Anal Sci; 2008 Jan; 24(1):139-48. PubMed ID: 18187863
[TBL] [Abstract][Full Text] [Related]
4. Retention prediction of adrenoreceptor agonists and antagonists on a diol column in hydrophilic interaction chromatography.
Quiming NS; Denola NL; Ueta I; Saito Y; Tatematsu S; Jinno K
Anal Chim Acta; 2007 Aug; 598(1):41-50. PubMed ID: 17693305
[TBL] [Abstract][Full Text] [Related]
5. Retention prediction of adrenoreceptor agonists and antagonists on unmodified silica phase in hydrophilic interaction chromatography.
Quiming NS; Denola NL; Saito Y; Jinno K
Anal Bioanal Chem; 2007 Aug; 388(8):1693-706. PubMed ID: 17583800
[TBL] [Abstract][Full Text] [Related]
6. Retention behavior of ginsenosides on a poly(vinyl alcohol)-bonded stationary phase in hydrophilic interaction chromatography.
Quiming NS; Denola NL; Soliev AB; Saito Y; Jinno K
Anal Bioanal Chem; 2007 Nov; 389(5):1477-88. PubMed ID: 17805518
[TBL] [Abstract][Full Text] [Related]
7. Combination of artificial neural network technique and linear free energy relationship parameters in the prediction of gradient retention times in liquid chromatography.
Fatemi MH; Abraham MH; Poole CF
J Chromatogr A; 2008 May; 1190(1-2):241-52. PubMed ID: 18395736
[TBL] [Abstract][Full Text] [Related]
8. The retention behavior of ginsenosides in HPLC and its application to quality assessment of Radix Ginseng.
Hu P; Luo GA; Wang Q; Zhao ZZ; Wang W; Jiang ZH
Arch Pharm Res; 2008 Oct; 31(10):1265-73. PubMed ID: 18958416
[TBL] [Abstract][Full Text] [Related]
9. Separation properties of novel and commercial polar stationary phases in hydrophilic interaction and reversed-phase liquid chromatography mode.
Wu J; Bicker W; Lindner W
J Sep Sci; 2008 May; 31(9):1492-503. PubMed ID: 18461572
[TBL] [Abstract][Full Text] [Related]
10. Quantitative structure migration relationship modeling of migration factor for some benzene derivatives in micellar electrokinetic chromatography.
Fatemi MH; Shamseddin H; Malekzadeh H
J Sep Sci; 2009 Jun; 32(11):1934-40. PubMed ID: 19425021
[TBL] [Abstract][Full Text] [Related]
11. Artificial neural network modelling of retention of pesticides in various octadecylsiloxane-bonded reversed-phase columns and water-acetonitrile mobile phase.
D'Archivio AA; Maggi MA; Mazzeo P; Ruggieri F
Anal Chim Acta; 2009 Jul; 646(1-2):47-61. PubMed ID: 19523555
[TBL] [Abstract][Full Text] [Related]
12. The retention behavior of ginsenosides in HPLC and its application to quality assessment of Radix Ginseng.
Hu P; Luo GA; Wang Q; Zhao ZZ; Wang W; Jiang ZH
Arch Pharm Res; 2009 May; 32(5):667-76. PubMed ID: 19471880
[TBL] [Abstract][Full Text] [Related]
13. Prediction of HPLC retention index using artificial neural networks and IGroup E-state indices.
Albaugh DR; Hall LM; Hill DW; Kertesz TM; Parham M; Hall LH; Grant DF
J Chem Inf Model; 2009 Apr; 49(4):788-99. PubMed ID: 19309176
[TBL] [Abstract][Full Text] [Related]
14. Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase.
Fatemi MH; Malekzadeh H; Shamseddin H
J Sep Sci; 2009 Feb; 32(4):653-9. PubMed ID: 19160374
[TBL] [Abstract][Full Text] [Related]
15. Modelling of retention of pesticides in reversed-phase high-performance liquid chromatography: quantitative structure-retention relationships based on solute quantum-chemical descriptors and experimental (solvatochromic and spin-probe) mobile phase descriptors.
D'Archivio AA; Ruggieri F; Mazzeo P; Tettamanti E
Anal Chim Acta; 2007 Jun; 593(2):140-51. PubMed ID: 17543600
[TBL] [Abstract][Full Text] [Related]
16. Prediction of immobilized artificial membrane-liquid chromatography retention of some drugs from their molecular structure descriptors and LFER parameters.
Fatemi MH; Shamseddin H
J Sep Sci; 2009 Oct; 32(20):3395-402. PubMed ID: 19750506
[TBL] [Abstract][Full Text] [Related]
17. Quantitative study of the structure-retention index relationship in the imine family.
Acevedo-Martínez J; Escalona-Arranz JC; Villar-Rojas A; Téllez-Palmero F; Pérez-Rosés R; González L; Carrasco-Velar R
J Chromatogr A; 2006 Jan; 1102(1-2):238-44. PubMed ID: 16288769
[TBL] [Abstract][Full Text] [Related]
18. Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes.
Arulsudar N; Subramanian N; Muthy RS
J Pharm Pharm Sci; 2005 Aug; 8(2):243-58. PubMed ID: 16124936
[TBL] [Abstract][Full Text] [Related]
19. Artificial neural network prediction of retention factors of some benzene derivatives and heterocyclic compounds in micellar electrokinetic chromatography.
Golmohammadi H; Fatemi MH
Electrophoresis; 2005 Sep; 26(18):3438-44. PubMed ID: 16110463
[TBL] [Abstract][Full Text] [Related]
20. Simplified extraction of ginsenosides from American ginseng (Panax quinquefolius L.) for high-performance liquid chromatography-ultraviolet analysis.
Corbit RM; Ferreira JF; Ebbs SD; Murphy LL
J Agric Food Chem; 2005 Dec; 53(26):9867-73. PubMed ID: 16366667
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]