Application of artificial neural networks for the prediction of sulfur polycyclic aromatic compounds retention indices

Autor:	Anatoly Dimoglo, Hatice Can, Vasyl Kovalishyn
Rok vydání:	2005
Předmět:	Chromatography Artificial neural network Stationary phase Chemistry Molecular descriptor Molecule Kovats retention index chemistry.chemical_element Physical and Theoretical Chemistry Condensed Matter Physics Biochemistry Sulfur
Zdroj:	Journal of Molecular Structure: THEOCHEM. 723:183-188
ISSN:	0166-1280
DOI:	10.1016/j.theochem.2005.03.004
Popis:	Quantitative models for structure–retention relationships have been developed for the retention indices of polycyclic aromatic sulfur heterocyclic compounds (PASHs). Six nonlinear models for predicting linear temperature programmed gas chromatographic retention characteristics on a Bpx5 (%5 phenyl) stationary phase. The developed predictive models relate molecular structure of each PASH compound to its experimental retention index.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::94279883fe7043a1032f8aad67f095fd https://doi.org/10.1016/j.theochem.2005.03.004 Zobrazit plný text záznamu Full Text from ScienceDirect