152 related articles for article (PubMed ID: 22901297)
1. Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids.
Fragkaki AG; Farmaki E; Thomaidis N; Tsantili-Kakoulidou A; Angelis YS; Koupparis M; Georgakopoulos C
J Chromatogr A; 2012 Sep; 1256():232-9. PubMed ID: 22901297
[TBL] [Abstract][Full Text] [Related]
2. Gas chromatographic quantitative structure-retention relationships of trimethylsilylated anabolic androgenic steroids by multiple linear regression and partial least squares.
Fragkaki AG; Tsantili-Kakoulidou A; Angelis YS; Koupparis M; Georgakopoulos C
J Chromatogr A; 2009 Nov; 1216(47):8404-20. PubMed ID: 19836752
[TBL] [Abstract][Full Text] [Related]
3. Prediction of capillary gas chromatographic retention times of fatty acid methyl esters in human blood using MLR, PLS and back-propagation artificial neural networks.
Gupta VK; Khani H; Ahmadi-Roudi B; Mirakhorli S; Fereyduni E; Agarwal S
Talanta; 2011 Jan; 83(3):1014-22. PubMed ID: 21147352
[TBL] [Abstract][Full Text] [Related]
4. Comparative multiple quantitative structure-retention relationships modeling of gas chromatographic retention time of essential oils using multiple linear regression, principal component regression, and partial least squares techniques.
Qin LT; Liu SS; Liu HL; Tong J
J Chromatogr A; 2009 Jul; 1216(27):5302-12. PubMed ID: 19486989
[TBL] [Abstract][Full Text] [Related]
5. Prediction of gas chromatographic retention indices of some amino acids and carboxylic acids from their structural descriptors.
Fatemi MH; Elyasi M
J Sep Sci; 2011 Nov; 34(22):3216-20. PubMed ID: 22012944
[TBL] [Abstract][Full Text] [Related]
6. Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.
Golmohammadi H
J Comput Chem; 2009 Nov; 30(15):2455-65. PubMed ID: 19360793
[TBL] [Abstract][Full Text] [Related]
7. Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.
Balabin RM; Smirnov SV
Anal Chim Acta; 2011 Apr; 692(1-2):63-72. PubMed ID: 21501713
[TBL] [Abstract][Full Text] [Related]
8. Prediction of gas chromatographic relative retention times of anabolic steroids.
Georgakopoulos CG; Tsika OG; Kiburis JC; Jurs PC
Anal Chem; 1991 Sep; 63(18):2025-8. PubMed ID: 1750703
[TBL] [Abstract][Full Text] [Related]
9. Cross-column prediction of gas-chromatographic retention of polychlorinated biphenyls by artificial neural networks.
D'Archivio AA; Incani A; Ruggieri F
J Chromatogr A; 2011 Dec; 1218(48):8679-90. PubMed ID: 22000780
[TBL] [Abstract][Full Text] [Related]
10. Comprehensive comparison of eight statistical modelling methods used in quantitative structure-retention relationship studies for liquid chromatographic retention times of peptides generated by protease digestion of the Escherichia coli proteome.
Zhou P; Tian F; Lv F; Shang Z
J Chromatogr A; 2009 Apr; 1216(15):3107-16. PubMed ID: 19232620
[TBL] [Abstract][Full Text] [Related]
11. Retention modelling of polychlorinated biphenyls in comprehensive two-dimensional gas chromatography.
D'Archivio AA; Incani A; Ruggieri F
Anal Bioanal Chem; 2011 Jan; 399(2):903-13. PubMed ID: 20972553
[TBL] [Abstract][Full Text] [Related]
12. Quantitative predictions of gas chromatography retention indexes with support vector machines, radial basis neural networks and multiple linear regression.
Chen HF
Anal Chim Acta; 2008 Feb; 609(1):24-36. PubMed ID: 18243870
[TBL] [Abstract][Full Text] [Related]
13. Multivariate optimization of a derivatisation procedure for the simultaneous determination of nine anabolic steroids by gas chromatography coupled with mass spectrometry.
Hadef Y; Kaloustian J; Portugal H; Nicolay A
J Chromatogr A; 2008 May; 1190(1-2):278-85. PubMed ID: 18353337
[TBL] [Abstract][Full Text] [Related]
14. Gas chromatography properties and mass spectrometry fragmentation of anabolic androgenic steroids in doping control.
Zhang Y; He G; Sheng L; Zhao X; Zhang Y; Zhang Y; Xu Y; Lu J
Bioanalysis; 2023 Jun; 15(12):661-671. PubMed ID: 37431827
[No Abstract] [Full Text] [Related]
15. Retention prediction of peptides based on uninformative variable elimination by partial least squares.
Put R; Daszykowski M; Baczek T; Vander Heyden Y
J Proteome Res; 2006 Jul; 5(7):1618-25. PubMed ID: 16823969
[TBL] [Abstract][Full Text] [Related]
16. Use of self-training artificial neural networks in modeling of gas chromatographic relative retention times of a variety of organic compounds.
Jalali-Heravi M; Garkani-Nejad Z
J Chromatogr A; 2002 Feb; 945(1-2):173-84. PubMed ID: 11860134
[TBL] [Abstract][Full Text] [Related]
17. Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure-retention relationships.
Put R; Vander Heyden Y
Anal Chim Acta; 2007 Oct; 602(2):164-72. PubMed ID: 17933600
[TBL] [Abstract][Full Text] [Related]
18. The study of the relationship between the new topological index A(m) and the gas chromatographic retention indices of hydrocarbons by artificial neural networks.
Li H; Zhang YX; Xu L
Talanta; 2005 Oct; 67(4):741-8. PubMed ID: 18970234
[TBL] [Abstract][Full Text] [Related]
19. Investigation of different linear and nonlinear chemometric methods for modeling of retention index of essential oil components: concerns to support vector machine.
Riahi S; Pourbasheer E; Ganjali MR; Norouzi P
J Hazard Mater; 2009 Jul; 166(2-3):853-9. PubMed ID: 19144466
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]