http://www.cnr.it/ontology/cnr/individuo/prodotto/ID33340
Multivariate optimization approach for chiral resolution of chlorophenoxy acid herbicides using teicoplanin as chiral selector in capillary LC (Articolo in rivista)
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- Multivariate optimization approach for chiral resolution of chlorophenoxy acid herbicides using teicoplanin as chiral selector in capillary LC (Articolo in rivista) (literal)
- Anno
- 2008-01-01T00:00:00+01:00 (literal)
- Alternative label
Noelia Rosales-Conrado, Mar1a Eugenia Leo´n-Gonza´lez, Luis Vicente Pe´rez-Arribas, Luis Maria Polo-Diez, Giovanni D'Orazio, Salvatore Fanali (2008)
Multivariate optimization approach for chiral resolution of chlorophenoxy acid herbicides using teicoplanin as chiral selector in capillary LC
in Chromatographia (Wiesb.)
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Noelia Rosales-Conrado, Mar1a Eugenia Leo´n-Gonza´lez, Luis Vicente Pe´rez-Arribas, Luis Maria Polo-Diez, Giovanni D'Orazio, Salvatore Fanali (literal)
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- Titolo
- Multivariate optimization approach for chiral resolution of chlorophenoxy acid herbicides using teicoplanin as chiral selector in capillary LC (literal)
- Abstract
- In this paper, the selectivity and resolution of enantiomeric separation by capillary liquid chromatography (cLC) of racemates of phenoxy acid herbicides are modelled. The compounds studied were 2-(±)-(2,4,5-trichlorophenoxy)propanoic acid (2,4,5-TP), 2-(±)-(2,4-
dichlorophenoxy)propanoic acid (2,4-DP),2-(±)-(4-chloro-2-methyl)phenoxypropanoic acid
(MCPP) and 2-(±)-[4-(2,4-dichlorophenoxy)phenoxy]propanoic acid] (diclofop acid), using a capillary column packed with silica particles modified with teicoplanin as chiral selector. Several mixtures of methanol (MeOH), water and triethylamine acetate (TEAA) buffer at
different pHs have been tested as mobile phases, and experimental parameters such as
resolution (Rs), retention factor (k) and enantioselectivity factor (a) have been measured in all
tested conditions. The chemometric methods used to explore and to model the data were
principal component analysis (PCA), stepwise multiple linear regression (stepwise-MLR) and
response surface analysis (RSA). The results show that it is possible to quantitatively predict the
quality of enantiomeric separations of related compounds in a given chromatographic system. (literal)
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